IMPACT OF URBAN DEVELOPMENT ON UV EXPOSURE: A CLUSTERING AND MACHINE LEARNING ASSESSMENT

Authors

Taufik Roni Sahroni Mr., Industrial Engineering Department, Bina Nusantara University, Jakarta 11480, Indonesia and Industrial Engineering Department, Universitas Buana Perjuangan Karawang, Jawa Barat 41361, IndonesiaFollow
Verdi Yasin, Department of Informatics Engineering, Jayakarta College of Information and Computer Management, Jakarta 10430, IndonesiaFollow
Lulut Alfaris, Department of Marine Technology, Politeknik Kelautan dan Perikanan Pangandaran, Jawa Barat 46396, IndonesiaFollow
Reza Ariefka, College of Teacher Training and Education Muhammadiyah OKU Timur, South Sumatra, Indonesia College of Teacher Training and Education Muhammadiyah OKU Timur, South Sumatra, 32382, IndonesiaFollow
Ruben Cornelius Siagian, Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Medan, Medan, 20221, IndonesiaFollow
Mohammad Alfin Karim, Industrial Engineering Department, Bina Nusantara University, Jakarta 11480, IndonesiaFollow
Nana Rahdiana, Industrial Engineering Department, Universitas Buana Perjuangan Karawang, Jawa Barat 41361, IndonesiaFollow
Ade Suhara, Industrial Engineering Department, Universitas Buana Perjuangan Karawang, Jawa Barat 41361, IndonesiaFollow

Author ORCID Identifier

Data ID Akun Sinta, Scopus, Orchid, dan Research Gate:

1. Taufik Roni Sahroni : https://sinta.kemdikbud.go.id/authors/profile/5977876/?view=garuda, https://www.scopus.com/authid/detail.uri?authorId=57223289031, https ://orcid.org/0000-0002-8497-3947

2. Lulut Alfaris: https://sinta.kemdikbud.go.id/authors/profile/6651674, https://www.scopus.com/authid/detail.uri?authorId=57218901939, https://orcid.org /0000-0002-1040-1727, https://www.researchgate.net/profile/Lulut-Alfaris

3. Ruben Cornelius Siagian: https://www.scopus.com/authid/detail.uri?authorId=58892111600, https://orcid.org/0000-0002-7307-7186, https://www.researchgate. net/profile/Ruben-Siagian

Article Classification

Environmental Science

Abstract

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling high-dimensional data and strong non-linear interactions, outperforming Random Forest in predicting Ultraviolet A variables. Analysis of variance (ANOVA) showed significant inter-group differences, which were validated by Tukey HSD post-hoc tests. Results showed that Cluster 4 was the region with the highest UV exposure. In contrast, Cluster 5 recorded the lowest, with exposure levels ranging from 6.61 to 15.82, a considerable difference of 9.21. The findings underscore the role of geographic and environmental factors in shaping UV exposure patterns, with implications for public health. Areas with high UV exposure face higher risks, including skin cancer and premature ageing. The predictive accuracy of the XGBoost model highlights its usefulness in addressing UV-related health risks. The study advocates for improved UV protection strategies and informed health policies to mitigate climate change impacts and promote sustainable urban development. The findings suggest that the development of data-driven early warning systems for UV radiation exposure could be implemented to improve public health policy and safety.

References

AbdelRahman, M. A. (2023). An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications. Rendiconti Lincei. Scienze Fisiche e Naturali, 34(3), 767–808. https://doi.org/10.1007/s12210-023-01155-3.

Agapito, G., Milano, M., & Cannataro, M. (2022). A python clustering analysis protocol of genes expression data sets. Genes, 13(10), 1839. DOI: 10.3390/genes13101839.

Ağbulut, Ü., Gürel, A. E., & Biçen, Y. (2021). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable and Sustainable Energy Reviews, 135, 110114. https://doi.org/10.1016/j.rser.2020.110114.

Aghaei, S., Gómez, A., & Vayanos, P. (2024). Strong optimal classification trees. Operations Research. https://doi.org/10.1287/opre.2021.0034.

Agus, C., Saktimulya, S. R., Dwiarso, P., Widodo, B., Rochmiyati, S., & Darmowiyono, M. (2021). Revitalization of local traditional culture for sustainable development of national character building in Indonesia. Innovations and Traditions for Sustainable Development, 347–369. https://doi.org/10.1007/978-3-030-78825-4_21.

Ahmad, F., & Ahmad, T. (2021). Image mining based on deep belief neural network and feature matching approach using Manhattan distance. Computer Assisted Methods in Engineering and Science, 28(2), 139–167. DOI:10.24423/cames.323.

Ahmed, A. M., Ahmed, M. H., Saha, S. K., Ahmed, O., & Sutradhar, A. (2022). Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model. Stochastic Environmental Research and Risk Assessment, 36(10), 3011–3039. DOI:10.1007/s00477-022-02177-3.

Akhanli, S. E., & Hennig, C. (2020). Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes. Statistics and Computing, 30(5), 1523–1544. https://doi.org/10.48550/arXiv.2002.01822.

Alfredsson, L., Armstrong, B. K., Butterfield, D. A., Chowdhury, R., de Gruijl, F. R., Feelisch, M., Garland, C. F., Hart, P. H., Hoel, D. G., & Jacobsen, R. (2020). Insufficient sun exposure has become a real public health problem. International Journal of Environmental Research and Public Health, 17(14), 5014. DOI: 10.3390/ijerph17145014.

Ali, L., Nawaz, A., Iqbal, S., Aamir Basheer, M., Hameed, J., Albasher, G., Shah, S. A. R., & Bai, Y. (2021). Dynamics of transit oriented development, role of greenhouse gases and urban environment: A study for management and policy. Sustainability, 13(5), 2536. DOI:10.3390/su13052536.

Arista, A. (2021). Comparison Decision Tree and Logistic Regression Machine Learning Classification Algorithms to determine Covid-19. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 6(1), 59–65. DOI:10.33395/sinkron.v7i1.11243.

Asarpota, K., & Nadin, V. (2020). Energy strategies, the urban dimension, and spatial planning. Energies, 13(14), 3642. https://doi.org/10.3390/en13143642.

Aurnab, A., Choudhury, S., Ruhan, S. R., Rifaiya Abrar, S. M., & Hossain Rabbi, S. (2023). Comparative analysis of machine learning techniques in optimal site selection.

Avdeef, A. (2020). Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database. ADMET and DMPK, 8(1), 29–77. DOI: 10.5599/admet.766.

Aziz, N. L. L. (2020). Relokasi ibu kota negara: Lesson learned dari negara lain. Jurnal Kajian Wilayah, 10(2), 37–64. DOI:10.14203/jkw.v10i2.827.

Bao, J., Lu, W., Duan, H., Ye, Y., Li, J., Liao, W., Li, Y., & Sun, Y. (2022). Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas. Frontiers in Immunology, 13, 933973. https://doi.org/10.3389/fimmu.2022.933973.

Belete, D. M., & Huchaiah, M. D. (2022). Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results. International Journal of Computers and Applications, 44(9), 875–886. https://doi.org/10.1080/1206212X.2021.1974663.

Benassi, M., Garofalo, S., Ambrosini, F., Sant’Angelo, R. P., Raggini, R., De Paoli, G., Ravani, C., Giovagnoli, S., Orsoni, M., & Piraccini, G. (2020). Using two-step cluster analysis and latent class cluster analysis to classify the cognitive heterogeneity of cross-diagnostic psychiatric inpatients. Frontiers in Psychology, 11, 1085. DOI: 10.3389/fpsyg.2020.01085.

Bernhard, G. H., Neale, R. E., Barnes, P. W., Neale, P., Zepp, R. G., Wilson, S. R., Andrady, A. L., Bais, A. F., McKenzie, R. L., & Aucamp, P. J. (2020a). Environmental effects of stratospheric ozone depletion, UV radiation and interactions with climate change: UNEP Environmental Effects Assessment Panel, update 2019. Photochemical & Photobiological Sciences, 19(5), 542–584. https://doi.org/10.1039/D0PP90011G.

Bernhard, G. H., Neale, R. E., Barnes, P. W., Neale, P., Zepp, R. G., Wilson, S. R., Andrady, A. L., Bais, A. F., McKenzie, R. L., & Aucamp, P. J. (2020b). Environmental effects of stratospheric ozone depletion, UV radiation and interactions with climate change: UNEP Environmental Effects Assessment Panel, update 2019. Photochemical & Photobiological Sciences, 19(5), 542–584. DOI: 10.1007/s43630-020-00001-x.

Bharti, J. P., Mishra, P., moorthy, U., Sathishkumar, V., Cho, Y., & Samui, P. (2021). Slope stability analysis using Rf, gbm, cart, bt and xgboost. Geotechnical and Geological Engineering, 39, 3741–3752. https://doi.org/10.1007/s10706-021-01721-2.

Bu, J., Liu, W., Pan, Z., & Ling, K. (2020). Comparative study of hydrochemical classification based on different hierarchical cluster analysis methods. International Journal of Environmental Research and Public Health, 17(24), 9515. https://doi.org/10.3390/ijerph17249515.

Camadini, L., Bouzid, Y., Merlet, M., & Carron, L. (2024). Randomness control and reproducibility study of random forest algorithm in R and Python. arXiv Preprint arXiv:2408.12184. https://doi.org/10.48550/arXiv.2408.12184.

Carleton, T., Cornetet, J., Huybers, P., Meng, K. C., & Proctor, J. (2021). Global evidence for ultraviolet radiation decreasing COVID-19 growth rates. Proceedings of the National Academy of Sciences, 118(1), e2012370118. DOI: 10.1073/pnas.2012370118.

Carrizosa, E., Molero-Río, C., & Romero Morales, D. (2021). Mathematical optimization in classification and regression trees. Top, 29(1), 5–33. DOI:10.1007/s11750-021-00594-1.

Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. DOI:10.38094/JASTT20165.

Cheah, J.-H., Thurasamy, R., Memon, M. A., Chuah, F., & Ting, H. (2020). Multigroup analysis using SmartPLS: Step-by-step guidelines for business research. Asian Journal of Business Research, 10(3), I–XIX. DOI:10.14707/ajbr.200087.

Chen, R.-C., Dewi, C., Huang, S.-W., & Caraka, R. E. (2020). Selecting critical features for data classification based on machine learning methods. Journal of Big Data, 7(1), 52. DOI:10.1186/s40537-020-00327-4.

Cherian, N. C., & Subasinghe, C. (2022). Sun-Safe Zones: Investigating Integrated Shading Strategies for Children’s Play Areas in Urban Parks. International Journal of Environmental Research and Public Health, 20(1), 114. DOI: 10.3390/ijerph20010114.

Chu, C.-T., & Hawkes, A. D. (2020). A geographic information system-based global variable renewable potential assessment using spatially resolved simulation. Energy, 193, 116630. https://doi.org/10.1016/j.energy.2019.116630.

Čuden, A. P., & Urbas, R. (2023). Advances in ultraviolet (UV) ray blocking textiles. In Functional and Technical Textiles (pp. 213–273). Elsevier. https://doi.org/10.1016/B978-0-323-91593-9.00013-4.

Cvekl, A., & Vijg, J. (2024). Aging of the eye: Lessons from cataracts and age-related macular degeneration. Ageing Research Reviews, 102407. DOI: 10.1016/j.arr.2024.102407.

Daberdaku, S., Tavazzi, E., & Di Camillo, B. (2020). A combined interpolation and weighted K-nearest neighbours approach for the imputation of longitudinal ICU laboratory data. Journal of Healthcare Informatics Research, 4(2), 174–188. DOI: 10.1007/s41666-020-00069-1.

Dalmaijer, E. S., Nord, C. L., & Astle, D. E. (2022). Statistical power for cluster analysis. BMC Bioinformatics, 23(1), 205. DOI: 10.1186/s12859-022-04675-1.

de Magalhães, L. P., & Rossi, F. (2024). Use of Indices in RGB and Random Forest Regression to Measure the Leaf Area Index in Maize. Agronomy, 14(4), 750. https://doi.org/10.3390/agronomy14040750.

de Paula Corrêa, M., Marciano, A. G., Carvalho, V. S. B., de Souza, P. M. B., Ripper, J. da S. C., Roy, D., Breton, L., & De Vecchi, R. (2021). Exposome extrinsic factors in the tropics: The need for skin protection beyond solar UV radiation. Science of the Total Environment, 782, 146921. https://doi.org/10.1016/j.scitotenv.2021.146921.

Deng, S.-Z., Jalaludin, B. B., Antó, J. M., Hess, J. J., & Huang, C.-R. (2020). Climate change, air pollution, and allergic respiratory diseases: A call to action for health professionals. Chinese Medical Journal, 133(13), 1552–1560. DOI: 10.1097/CM9.0000000000000861.

Dertinger, S. D., Briggs, E., Hussien, Y., Bryce, S. M., Avlasevich, S. L., Conrad, A., Johnson, G. E., Williams, A., & Bemis, J. C. (2024). Visualization strategies to aid interpretation of high‐dimensional genotoxicity data. Environmental and Molecular Mutagenesis. DOI: 10.1002/em.22604.

Diener, A., & Mudu, P. (2021). How can vegetation protect us from air pollution? A critical review on green spaces’ mitigation abilities for air-borne particles from a public health perspective-with implications for urban planning. Science of the Total Environment, 796, 148605. https://doi.org/10.1016/j.scitotenv.2021.148605.

Dodman, D., Hayward, B., Pelling, M., Castán Broto, V., & Chow, W. T. (2022). Cities, settlements and key infrastructure. https://doi.org/10.1017/9781009325844.008.

Dogan, A., & Birant, D. (2022). K-centroid link: A novel hierarchical clustering linkage method. Applied Intelligence, 1–24. https://doi.org/10.1007/s10489-021-02624-8.

Dombrovsky, L. A., Solovjov, V. P., & Webb, B. W. (2022). Effect of ground-based environmental conditions on the level of dangerous ultraviolet solar radiation. Journal of Quantitative Spectroscopy and Radiative Transfer, 279, 108048. DOI:10.1016/j.jqsrt.2021.108048.

Eleftheratos, K., Kapsomenakis, J., Fountoulakis, I., Zerefos, C. S., Jöckel, P., Dameris, M., Bais, A. F., Bernhard, G., Kouklaki, D., & Tourpali, K. (2022). Ozone, DNA-active UV radiation, and cloud changes for the near-global mean and at high latitudes due to enhanced greenhouse gas concentrations. Atmospheric Chemistry and Physics, 22(19), 12827–12855. https://doi.org/10.5194/acp-22-12827-2022.

Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big Data, 8, 1–37. DOI: 10.1186/s40537-021-00516-9.

Erlangga, A., Asmara, E. R. B., Amalia, R., & Rijanto, E. (2023). K-Means Clustering for Crime Cluster Analysis in District Prosecution Office of Indramayu. 1–6. DOI:10.1109/INCITEST59455.2023.10396984.

Falessi, D., Narayana, L., Thai, J. F., & Turhan, B. (2018). Preserving order of data when validating defect prediction models. California Polytechnic State University. https://arxiv.org/pdf/1809.01510v2.

Fard, F., & Sadeghi Naieni Fard, F. (2024). Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network. Remote Sensing, 16(2), 367. DOI:10.3390/rs16020367.

Farkas, G. (2020). Possibilities of using raster data in client‐side web maps. Transactions in GIS, 24(1), 72–84. https://doi.org/10.1111/tgis.12588.

Farooq, F., Ahmed, W., Akbar, A., Aslam, F., & Alyousef, R. (2021). Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners. Journal of Cleaner Production, 292, 126032. https://doi.org/10.1016/j.jclepro.2021.126032.

Fatima, S., Hussain, A., Amir, S. B., Ahmed, S. H., & Aslam, S. M. H. (2023). XGBoost and Random Forest Algorithms: An in Depth Analysis. Pakistan Journal of Scientific Research, 3(1), 26–31. DOI:10.57041/pjosr.v3i1.946.

Ferrara, F., Yan, X., Pecorelli, A., Guiotto, A., Colella, S., Pasqui, A., Lynch, S., Ivarsson, J., Anderias, S., & Choudhary, H. (2024). Combined exposure to UV and PM affect skin oxinflammatory responses and it is prevented by antioxidant mix topical application: Evidences from clinical study. Journal of Cosmetic Dermatology, 23(8), 2644–2656. https://doi.org/10.1111/jocd.16321.

Fredriksson, P. G., & Mohanty, A. (2021). Sunlight and culture. Journal of Economic Behavior & Organization, 188, 757–782. https://doi.org/10.1016/j.jebo.2021.05.033.

Frigione, M., & Rodríguez-Prieto, A. (2021). Can accelerated aging procedures predict the long term behavior of polymers exposed to different environments? Polymers, 13(16), 2688. DOI: 10.3390/polym13162688.

Fritz, M. (2023). Decision tree classification with missing values. https://doi.org/10.34726/hss.2023.105505.

Gajowniczek, K., & Ząbkowski, T. (2021). ImbTreeEntropy and ImbTreeAUC: Novel R packages for decision tree learning on the imbalanced datasets. Electronics, 10(6), 657. https://doi.org/10.3390/electronics10060657.

Ghiasi, M. M., & Zendehboudi, S. (2021). Application of decision tree-based ensemble learning in the classification of breast cancer. Computers in Biology and Medicine, 128, 104089. DOI: 10.1016/j.compbiomed.2020.104089.

Giordani, P., Ferraro, M. B., Martella, F., Giordani, P., Ferraro, M. B., & Martella, F. (2020). Hierarchical clustering. An Introduction to Clustering with R, 9–73. https://link.springer.com/book/10.1007/978-981-13-0553-5.

Głąbska, D., Kołota, A., Lachowicz, K., Skolmowska, D., Stachoń, M., & Guzek, D. (2021). The influence of vitamin D intake and status on mental health in children: A systematic review. Nutrients, 13(3), 952. DOI: 10.3390/nu13030952.

Görüş, V., Bahşı, M. M., & Çevik, M. (2024). Machine learning for the prediction of problems in steel tube bending process. Engineering Applications of Artificial Intelligence, 133, 108584. https://doi.org/10.1016/j.engappai.2024.108584.

Govender, P., & Sivakumar, V. (2020). Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmospheric Pollution Research, 11(1), 40–56. https://doi.org/10.1016/j.apr.2019.09.009.

Gunthe, S. S., Swain, B., Patra, S. S., & Amte, A. (2020). On the global trends and spread of the COVID-19 outbreak: Preliminary assessment of the potential relation between location-specific temperature and UV index. Journal of Public Health, 1–10. https://doi.org/10.1007/s10389-020-01279-y.

Gupta, S., Singh, A. K., Mishra, S., Vishnuram, P., Dharavat, N., Rajamanickam, N., Kalyan, C. N. S., AboRas, K. M., Sharma, N. K., & Bajaj, M. (2023). Estimation of solar radiation with consideration of terrestrial losses at a selected location—A review. Sustainability, 15(13), 9962. DOI:10.3390/su15139962.

Hadeed, S. J., O’rourke, M. K., Burgess, J. L., Harris, R. B., & Canales, R. A. (2020). Imputation methods for addressing missing data in short-term monitoring of air pollutants. Science of the Total Environment, 730, 139140. DOI: 10.1016/j.scitotenv.2020.139140.

Hamdia, K. M., Zhuang, X., & Rabczuk, T. (2021). An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Computing and Applications, 33(6), 1923–1933. https://doi.org/10.1007/s00521-020-05035-x.

Hasnain, M., Pasha, M. F., Ghani, I., Imran, M., Alzahrani, M. Y., & Budiarto, R. (2020). Evaluating trust prediction and confusion matrix measures for web services ranking. Ieee Access, 8, 90847–90861. https://doi.org/10.1109/ACCESS.2020.2994222.

Hassan, E., Shams, M. Y., Hikal, N. A., & Elmougy, S. (2023). The effect of choosing optimizer algorithms to improve computer vision tasks: A comparative study. Multimedia Tools and Applications, 82(11), 16591–16633. https://doi.org/10.1007/s11042-022-13820-0.

Hays, H., Flores, L. E., Kothari, V., Bilek, L., Geske, J., & Skinner, A. (2022). Vitamin D status and seasonal variation: A retrospective single institution database study of patients pursuing metabolic/bariatric surgery. Clinical Nutrition Open Science, 41, 1–9. https://doi.org/10.1016/j.nutos.2021.11.002.

Hodson, T. O. (2022). Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development Discussions, 2022, 1–10. https://doi.org/10.5194/gmd-15-5481-2022,%202022.

Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., & Soudry, D. (2020). Augment your batch: Improving generalization through instance repetition. 8129–8138. https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf

Holick, M. F. (2020). Sunlight, UV radiation, vitamin D, and skin cancer: How much sunlight do we need? Sunlight, Vitamin D and Skin Cancer, 19–36. DOI: 10.1007/978-3-030-46227-7_2.

Huang, A. H., & Chien, A. L. (2020). Photoaging: A review of current literature. Current Dermatology Reports, 9, 22–29. https://doi.org/10.1007/s13671-020-00288-0.

Isaia, G., Diémoz, H., Maluta, F., Fountoulakis, I., Ceccon, D., di Sarra, A., Facta, S., Fedele, F., Lorenzetto, G., & Siani, A. M. (2021). Does solar ultraviolet radiation play a role in COVID-19 infection and deaths? An environmental ecological study in Italy. Science of the Total Environment, 757, 143757. https://doi.org/10.1016/j.scitotenv.2020.143757.

Jin, T., Carbin, M., Roy, D., Frankle, J., & Dziugaite, G. K. (2022). Pruning’s effect on generalization through the lens of training and regularization. Advances in Neural Information Processing Systems, 35, 37947–37961. https://doi.org/10.48550/arXiv.2210.13738.

Jumaah, M., Abdulrazzaq, Z., & Abdul_Majeed, A. (2021). Role of green spaces and their impact on climate design and ecosystem efficiency. 761(1), 012055. DOI 10.1088/1755-1315/761/1/012055.

Kalalinggi, R., Hisdar, M., Sarmiasih, M., & Wijaya, A. K. (2023). Forecasting the development of IKN (new national capital) in sustainable development, Indonesia. Journal of Governance and Public Policy, 10(1), 48–59. https://doi.org/10.18196/jgpp.v10i1.16786.

Karna, A., & Gibert, K. (2022). Automatic identification of the number of clusters in hierarchical clustering. Neural Computing and Applications, 34(1), 119–134. https://doi.org/10.1007/s00521-021-05873-3.

Kavzoglu, T., & Teke, A. (2022a). Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost). Bulletin of Engineering Geology and the Environment, 81(5), 201. https://doi.org/10.1007/s10064-022-02708-w.

Kavzoglu, T., & Teke, A. (2022b). Predictive Performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arabian Journal for Science and Engineering, 47(6), 7367–7385. DOI:10.1007/s13369-022-06560-8.

Khan, J., Lee, E., & Kim, K. (2023). A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network. CAAI Transactions on Intelligence Technology, 8(4), 1124–1139. https://doi.org/10.1049/cit2.12148.

Kiangala, S. K., & Wang, Z. (2021). An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment. Machine Learning with Applications, 4, 100024. https://doi.org/10.1016/j.mlwa.2021.100024.

Knight, T., Price, S., Bowler, D., Hookway, A., King, S., Konno, K., & Richter, R. L. (2021). How effective is ‘greening’of urban areas in reducing human exposure to ground-level ozone concentrations, UV exposure and the ‘urban heat island effect’? An updated systematic review. Environmental Evidence, 10, 1–38. https://doi.org/10.1186/s13750-021-00226-y.

Knuschke, P. (2021). Sun exposure and vitamin D. Challenges in Sun Protection, 55, 296–315. DOI: 10.1159/000517640.

Korevaar, E., Kasza, J., Taljaard, M., Hemming, K., Haines, T., Turner, E. L., Thompson, J. A., Hughes, J. P., & Forbes, A. B. (2021). Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials. Clinical Trials, 18(5), 529–540. https://doi.org/10.1177/17407745211020852.

Kosmopoulos, P. G., Kazadzis, S., Schmalwieser, A. W., Raptis, P. I., Papachristopoulou, K., Fountoulakis, I., Masoom, A., Bais, A. F., Bilbao, J., & Blumthaler, M. (2021). Real-time UV index retrieval in Europe using Earth observation-based techniques: System description and quality assessment. Atmospheric Measurement Techniques, 14(8), 5657–5699. https://doi.org/10.5194/amt-14-5657-2021.

Krstinić, D., Braović, M., Šerić, L., & Božić-Štulić, D. (2020). Multi-label classifier performance evaluation with confusion matrix. Computer Science & Information Technology, 1, 1–14. DOI:10.5121/csit.2020.100801.

Kuhn, M., Johnson, K., Kuhn, M., & Johnson, K. (2013). Over-fitting and model tuning. Applied Predictive Modeling, 61–92. https://link.springer.com/book/10.1007/978-1-4614-6849-3.

Kya, M. A. (2024). Geospatial Patterns of Non-Melanoma Skin Cancer in Relation to Climate Changes in Iran. Asian Pacific Journal of Cancer Prevention: APJCP, 25(3), 1053. DOI: 10.31557/APJCP.2024.25.3.1053.

Lamrini, B. (2020). Contribution to Decision Tree Induction with Python: A Review. Data Mining-Methods, Applications and Systems, 21. DOI: 10.5772/intechopen.92438.

Lamy, K., Portafaix, T., Brogniez, C., Lakkala, K., Pitkänen, M. R., Arola, A., Forestier, J.-B., Amelie, V., Toihir, M. A., & Rakotoniaina, S. (2021). UV-Indien network: Ground-based measurements dedicated to the monitoring of UV radiation over the western Indian Ocean. Earth System Science Data, 13(9), 4275–4301. https://doi.org/10.5194/essd-13-4275-2021.

Laschewski, G., & Matzarakis, A. (2022). Weather-related human outdoor behavior with respect to solar ultraviolet radiation exposure in a changing climate. Atmosphere, 13(8), 1183. DOI:10.3390/atmos13081183.

Lee, Q. (2022). A web-based implementation of k-means algorithms. http://eprints.utar.edu.my/5010/1/1801846_LEE_QUAN.pdf.

Li, D., Wu, S., Liang, Z., & Li, S. (2020). The impacts of urbanization and climate change on urban vegetation dynamics in China. Urban Forestry & Urban Greening, 54, 126764. https://doi.org/10.1016/j.ufug.2020.126764.

Li, Y., Chu, X., Tian, D., Feng, J., & Mu, W. (2021). Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm. Applied Soft Computing, 113, 107924. https://doi.org/10.1016/j.asoc.2021.107924.

Liemohn, M. W., Shane, A. D., Azari, A. R., Petersen, A. K., Swiger, B. M., & Mukhopadhyay, A. (2021). RMSE is not enough: Guidelines to robust data-model comparisons for magnetospheric physics. Journal of Atmospheric and Solar-Terrestrial Physics, 218, 105624. https://doi.org/10.1016/j.jastp.2021.105624.

Liu, Z., Shie, C.-L., Li, A., & Meyer, D. (2020). NASA global satellite and model data products and services for tropical meteorology and climatology. Remote Sensing, 12(17), 2821. https://doi.org/10.3390/rs12172821.

Lucas‐Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P., Lemonsu, A., Kotlarski, S., & Caillaud, C. (2021). Convection‐permitting modeling with regional climate models: Latest developments and next steps. Wiley Interdisciplinary Reviews: Climate Change, 12(6), e731. DOI:10.1002/wcc.731.

Mahltig, B., Leisegang, T., Jakubik, M., & Haufe, H. (2023). Hybrid sol-gel materials for realization of radiation protective coatings—A review with emphasis on UV protective materials. Journal of Sol-Gel Science and Technology, 107(1), 20–31. https://doi.org/10.1007/s10971-021-05558-2.

Mariano, K., Dumalag, J., & Cadiz, N. (2024). ANALYSIS OF NOVASAR-1 S-BAND DATA IN DEVELOPING AN ALTERNATIVE LAND COVER MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 365–370. https://doi.org/10.5194/isprs-archives-XLVIII-4-W8-2023-365-2024.

Mattiev, J., Davityan, M., & Kavsek, B. (2023). ACMKC: A Compact Associative Classification Model Using K-Modes Clustering with Rule Representations by Coverage. Mathematics, 11(18), 3978. https://doi.org/10.3390/math11183978.

Maynard, D. S., Bialic-Murphy, L., Zohner, C. M., Averill, C., van den Hoogen, J., Ma, H., Mo, L., Smith, G. R., Acosta, A. T., & Aubin, I. (2022). Global relationships in tree functional traits. Nature Communications, 13(1), 3185. DOI: 10.1038/s41467-022-30888-2.

Mazlan, A. D. N., Hairuddin, M. A., Md Tahir, N., Khirul Ashar, N. D., & Jusoh, M. H. (2020). Comparative analysis of PCA and ANOVA for assessing the subset feature selection of the geomagnetic Disturbance Storm Time. Journal of Electrical and Electronic Systems Research (JEESR), 17, 8–16. https://ir.uitm.edu.my/id/eprint/42379.

Meedeniya, D., Jayanetti, J. M., Dilini, M., Wickramapala, M., & Madushanka, J. (2020). Land‐Use Classification with Integrated Data. Machine Vision Inspection Systems: Image Processing, Concepts, Methodologies and Applications, 1, 1–36. https://www.oreilly.com/library/view/machine-vision-inspection/9781119681809/c01.xhtml.

Minisola, S., Colangelo, L., Pepe, J., Diacinti, D., Cipriani, C., & Rao, S. D. (2021). Osteomalacia and vitamin D status: A clinical update 2020. Journal of Bone and Mineral Research Plus, 5(1), e10447. DOI: 10.1002/jbm4.10447.

Mishra, S. (2022). An optimized gradient boost decision tree using enhanced African buffalo optimization method for cyber security intrusion detection. Applied Sciences, 12(24), 12591. https://doi.org/10.3390/app122412591.

Morrissey, K. L., Iveša, L., Delva, S., D’hondt, S., Willems, A., & De Clerck, O. (2021). Impacts of environmental stress on resistance and resilience of algal‐associated bacterial communities. Ecology and Evolution, 11(21), 15004–15019. DOI: 10.1002/ece3.8184.

Mwanzia, D. (2021). A study of solar variability and its effects on Earth’s Climate. http://erepository.uonbi.ac.ke:8080/bitstream/handle/11295/155919/Mwanzia_A%20study%20of%20solar%20variability%20and%20its%20effects%20on%20Earth%E2%80%99s%20Climate.pdf?sequence=1.

Nanda, A., Mohapatra, B. B., Mahapatra, A. P. K., Mahapatra, A. P. K., & Mahapatra, A. P. K. (2021). Multiple comparison test by Tukey’s honestly significant difference (HSD): Do the confident level control type I error. International Journal of Statistics and Applied Mathematics, 6(1), 59–65. DOI:10.22271/MATHS.2021.V6.I1A.636.

Nandapala, E., & Jayasena, K. (2020). The practical approach in Customers segmentation by using the K-Means Algorithm. 344–349. DOI: 10.1109/ICIIS51140.2020.9342639.

Neale, R. E., Barnes, P. W., Robson, T. M., Neale, P. J., Williamson, C. E., Zepp, R. G., Wilson, S. R., Madronich, S., Andrady, A. L., & Heikkilä, A. M. (2021). Environmental effects of stratospheric ozone depletion, UV radiation, and interactions with climate change: UNEP Environmental Effects Assessment Panel, Update 2020. Photochemical & Photobiological Sciences, 20(1), 1–67. DOI: 10.1007/s43630-022-00176-5.

Neale, R., Lucas, R., Byrne, S., Hollestein, L., Rhodes, L., Yazar, S., Young, A., Berwick, M., Ireland, R., & Olsen, C. (2023). The effects of exposure to solar radiation on human health. Photochemical & Photobiological Sciences, 22(5), 1011–1047. DOI: 10.1007/s43630-023-00375-8.

Nti, I. K., Nyarko-Boateng, O., & Aning, J. (2021). Performance of machine learning algorithms with different K values in K-fold CrossValidation. International Journal of Information Technology and Computer Science, 13(6), 61–71. DOI:10.5815/ijitcs.2021.06.05.

Nugroho, S. (2020). The Divergent Nation of Indonesia. Springer. https://link.springer.com/book/10.1007/978-981-15-4242-8.

Olatunji, A. O., Olaboye, J. A., Maha, C. C., Kolawole, T. O., & Abdul, S. (2024). Environmental microbiology and public health: Advanced strategies for mitigating waterborne and airborne pathogens to prevent disease. International Medical Science Research Journal, 4(7), 756–770. DOI:10.51594/imsrj.v4i7.1355.

Parel, N. S., Krishna, P. V., Gupta, A., Uthayaseelan, K., Uthayaseelan, K., Kadari, M., Subhan, M., & Kasire, S. P. (2022). Depression and vitamin D: a peculiar relationship. Cureus, 14(4). DOI: 10.7759/cureus.24363.

Parker, E. R. (2021). The influence of climate change on skin cancer incidence–a review of the evidence. International Journal of Women’s Dermatology, 7(1), 17–27. https://doi.org/10.1016/j.ijwd.2020.07.003.

Paumelle, M., Occelli, F., Wakim, L., Brousmiche, D., Bouhadj, L., Ternynck, C., Lanier, C., Cuny, D., & Deram, A. (2023). Description of the multi-dimensional environment at the territorial scale: A holistic framework using cluster analysis and open data in France. Ecological Indicators, 154, 110562. https://doi.org/10.1016/j.ecolind.2023.110562.

Peel, J. L., Haeuber, R., Garcia, V., Russell, A. G., & Neas, L. (2013). Impact of nitrogen and climate change interactions on ambient air pollution and human health. Biogeochemistry, 114, 121–134. https://www.jstor.org/stable/24715094.

Peng, H., Zhang, X., Ren, W., & He, J. (2023). Spatial pattern and driving factors of cropland ecosystem services in a major grain-producing region: A production-living-ecology perspective. Ecological Indicators, 155, 111024. https://doi.org/10.1016/j.ecolind.2023.111024.

Pfeifer, G. P. (2020). Mechanisms of UV-induced mutations and skin cancer. Genome Instability & Disease, 1(3), 99–113. DOI: 10.1007/s42764-020-00009-8.

Picornell, A., Oteros, J., Ruiz-Mata, R., Recio, M., Trigo, M., Martínez-Bracero, M., Lara, B., Serrano-García, A., Galán, C., & García-Mozo, H. (2021). Methods for interpolating missing data in aerobiological databases. Environmental Research, 200, 111391. DOI: 10.1016/j.envres.2021.111391.

Polzonetti, V., Pucciarelli, S., Vincenzetti, S., & Polidori, P. (2020). Dietary intake of vitamin D from dairy products reduces the risk of osteoporosis. Nutrients, 12(6), 1743. DOI: 10.3390/nu12061743.

Priyanka, & Kumar, D. (2020). Decision tree classifier: A detailed survey. International Journal of Information and Decision Sciences, 12(3), 246–269. https://doi.org/10.1504/IJIDS.2020.108141.

Probst, P., & Boulesteix, A.-L. (2018). To tune or not to tune the number of trees in random forest. Journal of Machine Learning Research, 18(181), 1–18. https://doi.org/10.48550/arXiv.1705.05654.

Rácz, A., Bajusz, D., & Héberger, K. (2021). Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification. Molecules, 26(4), 1111. DOI: 10.3390/molecules26041111.

Rebuli, K. B., Ozella, L., Vanneschi, L., & Giacobini, M. (2023). Multi-algorithm clustering analysis for characterizing cow productivity on automatic milking systems over lactation periods. Computers and Electronics in Agriculture, 211, 108002. https://dx.doi.org/10.2139/ssrn.4435365.

Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., & Thuiller, W. (2017). Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929. doi: 10.1111/ecog.02881.

Roshan, D. R., Koc, M., Abdallah, A., Martin-Pomares, L., Isaifan, R., & Fountoukis, C. (2020). UV index forecasting under the influence of desert dust: Evaluation against surface and satellite-retrieved data. Atmosphere, 11(1), 96. DOI:10.3390/atmos11010096.

Roziika, A., & Nurwati, R. N. (2020). „Pengaruh Penerapan Sustainable Development Goals Terhadap Kependudukan Di Indonesia Dalam Membangun Perekonomian Nasional‟. Program Studi Ilmu Kesejahteraan Sosial Universitas Padjadjaran, 6, 1–16.

Ruswandi, D., Syafii, M., Maulana, H., Ariyanti, M., Indriani, N. P., & Yuwariah, Y. (2021). GGE biplot analysis for stability and adaptability of maize hybrids in western region of Indonesia. International Journal of Agronomy, 2021(1), 2166022. DOI:10.1155/2021/2166022.

Sa’adi, Z., Shahid, S., & Shiru, M. S. (2021). Defining climate zone of Borneo based on cluster analysis. Theoretical and Applied Climatology, 145, 1467–1484. https://doi.org/10.1007/s00704-021-03701-1.

Sagala, N. T., & Gunawan, A. A. S. (2022). Discovering the optimal number of crime cluster using elbow, silhouette, gap statistics, and nbclust methods. ComTech: Computer, Mathematics and Engineering Applications, 13(1), 1–10. DOI:10.21512/comtech.v13i1.7270.

Šakić Trogrlić, R., van den Homberg, M., Budimir, M., McQuistan, C., Sneddon, A., & Golding, B. (2022). Early warning systems and their role in disaster risk reduction. In Towards the “perfect” weather warning: Bridging disciplinary gaps through partnership and communication (pp. 11–46). Springer International Publishing Cham. DOI:10.1007/978-3-030-98989-7.

Salvadori, G., Leccese, F., Lista, D., Burattini, C., & Bisegna, F. (2020). Use of smartphone apps to monitor human exposure to solar radiation: Comparison between predicted and measured UV index values. Environmental Research, 183, 109274. DOI: 10.1016/j.envres.2020.109274.

Sancho, A., Ribeiro, J., Reis, M. S., & Martins, F. G. (2022a). Cluster analysis of crude oils with k-means based on their physicochemical properties. Computers & Chemical Engineering, 157, 107633. https://doi.org/10.1016/j.compchemeng.2021.107633.

Sancho, A., Ribeiro, J., Reis, M. S., & Martins, F. G. (2022b). Cluster analysis of crude oils with k-means based on their physicochemical properties. Computers & Chemical Engineering, 157, 107633. https://doi.org/10.1016/j.compchemeng.2021.107633.

Sangaiah, A. K., Javadpour, A., Ja’fari, F., Zhang, W., & Khaniabadi, S. M. (2022). Hierarchical clustering based on dendrogram in sustainable transportation systems. IEEE Transactions on Intelligent Transportation Systems. DOI:10.1109/TITS.2022.3222789.

Sanjari, E., Majidian Dehkordi, F., & Raeisi Shahraki, H. (2023). Clustering undergraduate students based on academic burnout and satisfaction from the field using partitioning around Medoid. Computational and Mathematical Methods in Medicine, 2023(1), 8898939. DOI: 10.1155/2023/8898939.

Sarker, I. H., Colman, A., Han, J., Khan, A. I., Abushark, Y. B., & Salah, K. (2020). Behavdt: A behavioral decision tree learning to build user-centric context-aware predictive model. Mobile Networks and Applications, 25, 1151–1161. DOI:10.1007/s11036-019-01443-z.

Sarmadi, H., & Karamodin, A. (2020). A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects. Mechanical Systems and Signal Processing, 140, 106495. https://doi.org/10.1016/j.ymssp.2019.106495.

Sasso, F. C., Simeon, V., Galiero, R., Caturano, A., De Nicola, L., Chiodini, P., Rinaldi, L., Salvatore, T., Lettieri, M., & Nevola, R. (2022). The number of risk factors not at target is associated with cardiovascular risk in a type 2 diabetic population with albuminuria in primary cardiovascular prevention. Post-hoc analysis of the NID-2 trial. Cardiovascular Diabetology, 21(1), 235. DOI: 10.1186/s12933-022-01674-7.

Sfîcă, L., Bulai, M., Amihăesei, V.-A., Ion, C., & Ștefan, M. (2020). Weather conditions (with focus on UV radiation) associated with COVID-19 outbreak and worldwide climate-based prediction for future prevention. Aerosol and Air Quality Research, 20(9), 1862–1873. https://doi.org/10.4209/aaqr.2020.05.0206.

Shahapure, K. R., & Nicholas, C. (2020). Cluster quality analysis using silhouette score. 747–748. DOI: 10.1109/DSAA49011.2020.00096.

Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. Eurasip Journal on Wireless Communications and Networking, 2021, 1–16. https://doi.org/10.1186/s13638-021-01910-w.

Shi, Y., Li, J., & Li, Z. (2018). Gradient boosting with piece-wise linear regression trees. arXiv Preprint arXiv:1802.05640. https://doi.org/10.48550/arXiv.1802.05640.

Shutaywi, M., & Kachouie, N. N. (2021a). Silhouette analysis for performance evaluation in machine learning with applications to clustering. Entropy, 23(6), 759. https://doi.org/10.3390/e23060759.

Shutaywi, M., & Kachouie, N. N. (2021b). Silhouette analysis for performance evaluation in machine learning with applications to clustering. Entropy, 23(6), 759. https://doi.org/10.3390/e23060759.

Singh, R. (2023). Impact of climate and land use land cover changes on soil erosion. In Soil and Water Conservation Structures Design (pp. 415–441). Springer.

Siomos, N., Fountoulakis, I., Natsis, A., Drosoglou, T., & Bais, A. (2020). Automated aerosol classification from spectral UV measurements using machine learning clustering. Remote Sensing, 12(6), 965. DOI:10.3390/rs12060965.

Smith, L. N., & Topin, N. (2019). Super-convergence: Very fast training of neural networks using large learning rates. 11006, 369–386. https://doi.org/10.48550/arXiv.1708.07120.

Snyder, A., Valdebran, M., Terrero, D., Amber, K. T., & Kelly, K. M. (2020). Solar ultraviolet exposure in individuals who perform outdoor sport activities. Sports Medicine-Open, 6, 1–12. DOI: 10.1186/s40798-020-00272-9.

Sokhi, R. S., Moussiopoulos, N., Baklanov, A., Bartzis, J., Coll, I., Finardi, S., Friedrich, R., Geels, C., Grönholm, T., & Halenka, T. (2021). Advances in air quality research–current and emerging challenges. Atmospheric Chemistry and Physics Discussions, 2021, 1–133. https://acp.copernicus.org/preprints/acp-2021-581/acp-2021-581.pdf.

Stackhouse, P. W., Macpherson, B., Broddle, M., McNeil, C., Barnett, A. J., Mikovitz, C., & Zhang, T. (2021). Introduction to the Prediction Of Worldwide Energy Resources (POWER) Project: NASA Applied Sciences Week 2021. 2021 NASA Applied Sciences Week. https://ntrs.nasa.gov/citations/20210023937.

Syahab, A. S., Pamungkas, G. L., & Akmal, S. (2023). The Ultraviolet Index Classification Using Logistic Regression and Random Forest Methods for Predicting Extreme Conditions (Nos. 2516–2314). EasyChair. https://easychair.org/publications/preprint/TfZq/open.

Szeghalmy, S., & Fazekas, A. (2023). A comparative study of the use of stratified cross-validation and distribution-balanced stratified cross-validation in imbalanced learning. Sensors, 23(4), 2333. DOI: 10.3390/s23042333.

Tandon, R., Vashist, P., Gupta, N., Gupta, V., Sahay, P., Deka, D., Singh, S., Vishwanath, K., & Murthy, G. (2020). Association of dry eye disease and sun exposure in geographically diverse adult (≥ 40 years) populations of India: The SEED (sun exposure, environment and dry eye disease) study-Second report of the ICMR-EYE SEE study group. The Ocular Surface, 18(4), 718–730. DOI: 10.1016/j.jtos.2020.07.016.

Tandon, R., Vashist, P., Gupta, N., Gupta, V., Yadav, S., Deka, D., Singh, S., Vishwanath, K., & Murthy, G. (2022). The association of sun exposure, ultraviolet radiation effects and other risk factors for pterygium (the SURE RISK for pterygium study) in geographically diverse adult (≥ 40 years) rural populations of India-3rd report of the ICMR-EYE SEE study group. PloS One, 17(7), e0270065. DOI:10.1371/journal.pone.0270065.

Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11(2), 612–619. DOI:10.14569/IJACSA.2020.0110277.

Teng, Y., Yu, Y., Li, S., Huang, Y., Xu, D., Tao, X., & Fan, Y. (2021). Ultraviolet radiation and basal cell carcinoma: An environmental perspective. Frontiers in Public Health, 9, 666528. https://doi.org/10.3389/fpubh.2021.666528.

Thomas, T., P. Vijayaraghavan, A., Emmanuel, S., Thomas, T., P. Vijayaraghavan, A., & Emmanuel, S. (2020). Applications of decision trees. Machine Learning Approaches in Cyber Security Analytics, 157–184. https://link.springer.com/book/10.1007/978-981-15-1706-8.

Tırınk, C., Önder, H., Francois, D., Marcon, D., Şen, U., Shaikenova, K., Omarova, K., & Tyasi, T. L. (2023). Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed. Plos One, 18(8), e0289348. https://doi.org/10.1371/journal.pone.0289348.

Tran, Q. T., Breuer, A., Lin, T., Tatevossian, R., Allen, S. J., Clay, M., Furtado, L. V., Chen, M., Hedges, D., & Michael, T. (2024). Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors. NPJ Precision Oncology, 8(1), 218. https://doi.org/10.1038/s41698-024-00718-3.

Turner, J., Igoe, D., Parisi, A. V., McGonigle, A. J., Amar, A., & Wainwright, L. (2020). A review on the ability of smartphones to detect ultraviolet (UV) radiation and their potential to be used in UV research and for public education purposes. Science of the Total Environment, 706, 135873. DOI: 10.1016/j.scitotenv.2019.135873.

Uçar, M. K., Nour, M., Sindi, H., & Polat, K. (2020). The effect of training and testing process on machine learning in biomedical datasets. Mathematical Problems in Engineering, 2020(1), 2836236. https://doi.org/10.1155/2020/2836236.

Umar, S. A., & Tasduq, S. A. (2022). Ozone layer depletion and emerging public health concerns-an update on epidemiological perspective of the ambivalent effects of ultraviolet radiation exposure. Frontiers in Oncology, 12, 866733. DOI: 10.3389/fonc.2022.866733.

Vitt, R., Laschewski, G., Bais, A. F., Diémoz, H., Fountoulakis, I., Siani, A.-M., & Matzarakis, A. (2020). UV-Index climatology for Europe based on satellite data. Atmosphere, 11(7), 727. https://doi.org/10.3390/atmos11070727.

Vuilleumier, L., Harris, T., Nenes, A., Backes, C., & Vernez, D. (2021). Developing a UV climatology for public health purposes using satellite data. Environment International, 146, 106177. DOI: 10.1016/j.envint.2020.106177.

Vujović, Ž. (2021). Classification model evaluation metrics. International Journal of Advanced Computer Science and Applications, 12(6), 599–606. DOI:10.14569/IJACSA.2021.0120670.

Wang, J., Li, J., Liu, S., Li, H., Chen, X., Peng, C., Zhang, P., & Liu, X. (2021). Distinct microplastic distributions in soils of different land-use types: A case study of Chinese farmlands. Environmental Pollution, 269, 116199. https://doi.org/10.1016/j.envpol.2020.116199.

Wang, L., Zhu, L., Jiang, J., Wang, L., & Ni, W. (2021). Decision tree analysis for evaluating disease activity in patients with rheumatoid arthritis. Journal of International Medical Research, 49(10), 03000605211053232. https://doi.org/10.1177/03000605211053232.

Wang, S., Zhuang, J., Zheng, J., Fan, H., Kong, J., & Zhan, J. (2021). Application of Bayesian hyperparameter optimized random forest and XGBoost model for landslide susceptibility mapping. Frontiers in Earth Science, 9, 712240. https://doi.org/10.3389/feart.2021.712240.

Wang, X., Zhang, C., Qiang, Z., Xu, W., & Fan, J. (2024). A New Forest Growing Stock Volume Estimation Model Based on AdaBoost and Random Forest Model. Forests, 15(2), 260. https://doi.org/10.3390/f15020260.

Wang, Z., Sun, Y., Yao, W., Ba, Q., & Wang, H. (2021). Effects of cadmium exposure on the immune system and immunoregulation. Frontiers in Immunology, 12, 695484. https://doi.org/10.3389/fimmu.2021.695484.

Wu, L., Li, X., Yuan, J., & Wang, S. (2023). Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm. Frontiers of Structural and Civil Engineering, 17(12), 1777–1795. DOI:10.1007/s11709-023-0044-4.

Wulf, H. C. (2009). Sunscreens. Non-Surgical Treatment of Keratinocyte Skin Cancer, 167–176. https://doi.org/10.1007/978-3-540-79341-0_21.

Xu, H., Zeng, P., Sun, F., Zhao, H., & Che, Y. (2024). Population exposure disparities between residential and working areas during a cold wave event in Changchun, China. Applied Geography, 172, 103433. DOI:10.1016/j.apgeog.2024.103433.

Xu, R., Rahmandad, H., Gupta, M., DiGennaro, C., Ghaffarzadegan, N., Amini, H., & Jalali, M. S. (2021). Weather, air pollution, and SARS-CoV-2 transmission: A global analysis. The Lancet Planetary Health, 5(10), e671–e680. DOI: https://doi.org/10.1016/S2542-5196(21)00202-3.

Xu, Y., & Goodacre, R. (2018). On splitting training and validation set: A comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. Journal of Analysis and Testing, 2(3), 249–262. DOI: 10.1007/s41664-018-0068-2.

Yadav, D. C., & Pal, S. (2020). Prediction of thyroid disease using decision tree ensemble method. Human-Intelligent Systems Integration, 2, 89–95. https://doi.org/10.1007/s42454-020-00006-y.

Yan, T., Shen, S.-L., Zhou, A., & Chen, X. (2022). Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm. Journal of Rock Mechanics and Geotechnical Engineering, 14(4), 1292–1303. https://doi.org/10.1016/j.jrmge.2022.03.002.

Yang, J., & Guan, J. (2022). A heart disease prediction model based on feature optimization and smote-Xgboost algorithm. Information, 13(10), 475. https://doi.org/10.3390/info13100475.

Yang, J., Li, Z., Zhai, P., Zhao, Y., & Gao, X. (2020). The influence of soil moisture and solar altitude on surface spectral albedo in arid area. Environmental Research Letters, 15(3), 035010. https://ui.adsabs.harvard.edu/link_gateway/2020ERL....15c5010Y/doi:10.1088/1748-9326/ab6ae2.

Yousif, A., Drou, N., Rowe, J., Khalfan, M., & Gunsalus, K. C. (2020). NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization. Bmc Bioinformatics, 21, 1–14. DOI: 10.1186/s12859-020-03577-4.

Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X. (2022). Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron, 110(1), 21–35. DOI: 10.1016/j.neuron.2021.10.030.

Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., & Kycko, M. (2021). Comparison of random forest, support vector machines, and neural networks for post-disaster forest species mapping of the krkonoše/karkonosze transboundary biosphere reserve. Remote Sensing, 13(13), 2581. https://doi.org/10.3390/rs13132581.

Zhang, Y., Zhang, Y., & Li, Z. (2022). A novel productive double skin façades for residential buildings: Concept, design and daylighting performance investigation. Building and Environment, 212, 108817. https://doi.org/10.1016/j.buildenv.2022.108817.

Zou, W., Sastry, M., Gooding, J. J., Ramanathan, R., & Bansal, V. (2020). Recent advances and a roadmap to wearable UV sensor technologies. Advanced Materials Technologies, 5(4), 1901036. https://doi.org/10.1002/admt.201901036.

Recommended Citation

Sahroni, Taufik Roni Mr.; Yasin, Verdi; Alfaris, Lulut; Ariefka, Reza; Siagian, Ruben Cornelius; Karim, Mohammad Alfin; Rahdiana, Nana; and Suhara, Ade (2024). IMPACT OF URBAN DEVELOPMENT ON UV EXPOSURE: A CLUSTERING AND MACHINE LEARNING ASSESSMENT. Journal of Environmental Science and Sustainable Development, 7(2), 686-727.
Available at: https://doi.org/10.7454/jessd.v7i2.1258