Alahmad, B., Vicedo-Cabrera, A. M., Chen, K., Garshick, E., Bernstein, A. S., Schwartz, J., & Koutrakis, P. (2022). Climate change and health in Kuwait: Temperature and mortality projections under different climatic scenarios. Environmental Research Letters, 17(7). https://doi.org/10.1088/1748-9326/ac7601

Al-Awadhi, J. M. (2001). Impact of gravel quarrying on the desert environment of Kuwait. Environmental Geology, 41(3–4), 365–371. https://doi.org/10.1007/s002540100398

AlDousari, A. E., Kafy, A.-A., Saha, M., Fattah, M. A., Almulhim, A. I., Faisal, A.-A., al Rakib, A., Jahir, D. M. A., Rahaman, Z. A., Bakshi, A., Shahrier, M., & Rahman, M. M. (2022). Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait. Sustainable Cities and Society, 86. https://doi.org/10.1016/j.scs.2022.104107

Al-Nakeeb, Y., Lyons, M., Dodd, L. J., & Al-Nuaim, A. (2015). An investigation into the lifestyle, health habits and risk factors of young adults. International Journal of Environmental Research and Public Health, 12(4), 4380–4394. https://doi.org/10.3390/ijerph120404380

Al-sharif, A. A. A., & Pradhan, B. (2015). A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS. Geocarto International, 30(8), 858–881. https://doi.org/10.1080/10106049.2014.997308

Amit, S., Kafy, A.-A., & Barua, L. (2022). Systemic Barriers to Financial Inclusion in the Banking Sector of Bangladesh. In Business and Management in Asia: Digital Innovation and Sustainability. https://doi.org/10.1007/978-981-19-6418-3_8

Ashtiani, A., Mirzaei, P. A., & Haghighat, F. (2014). Indoor thermal condition in urban heat island: Comparison of the artificial neural network and regression methods prediction. Energy and Buildings, 76, 597–604. https://doi.org/10.1016/j.enbuild.2014.03.018

Bennett, K. P., & Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1(1), 23–34. https://doi.org/10.1080/10556789208805504

Bokaie, M., Zarkesh, M. K., Arasteh, P. D., & Hosseini, A. (2016). Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use/ Land Cover in Tehran. Sustainable Cities and Society, 23, 94–104. https://doi.org/10.1016/j.scs.2016.03.009

Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). Training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, 144–152.

Caballero, C. B., Ruhoff, A., & Biggs, T. (2022). Land use and land cover changes and their impacts on surface-atmosphere interactions in Brazil: A systematic review. Science of the Total Environment, 808. https://doi.org/10.1016/j.scitotenv.2021.152134

Camargo, F. F., Sano, E. E., Almeida, C. M., Mura, J. C., & Almeida, T. (2019). A comparative assessment of machine-learning techniques for land use and land cover classification of the Brazilian tropical savanna using ALOS-2/PALSAR-2 polarimetric images. Remote Sensing, 11(13). https://doi.org/10.3390/rs11131600

Carranza-García, M., García-Gutiérrez, J., & Riquelme, J. C. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sensing, 11(3). https://doi.org/10.3390/rs11030274

Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Wyszogrodzki, A. A., & Zhang, C. (2011). The integrated WRF/urban modelling system: Development, evaluation, and applications to urban environmental problems. International Journal of Climatology, 31(2), 273–288. https://doi.org/10.1002/joc.2158

Chen, Y., Yang, J., Yang, R., Xiao, X., & Xia, J. C. (2022). Contribution of urban functional zones to the spatial distribution of urban thermal environment. Building and Environment, 216. https://doi.org/10.1016/j.buildenv.2022.109000

de Jong, S. M., Shen, Y., de Vries, J., Bijnaar, G., van Maanen, B., Augustinus, P., & Verweij, P. (2021). Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. International Journal of Applied Earth Observation and Geoinformation, 97. https://doi.org/10.1016/j.jag.2020.102293

Dissanayake, D. M. S. L. B., Morimoto, T., Ranagalage, M., & Murayama, Y. (2019). Land-use/land-cover changes and their impact on surface urban heat islands: Case study of Kandy City, Sri Lanka. Climate, 7(8). https://doi.org/10.3390/cli7080099

Elmahdy, S., Mohamed, M., & Ali, T. (2020). Land use/land cover changes impact on groundwater level and quality in the northern part of the United Arab Emirates. Remote Sensing, 12(11). https://doi.org/10.3390/rs12111715

Eniolorunda, N. B., Mashi, S. A., & Nsofor, G. N. (2017). Toward achieving a sustainable management: characterization of land use/land cover in Sokoto Rima floodplain, Nigeria. Environment, Development and Sustainability, 19(5), 1855–1878. https://doi.org/10.1007/s10668-016-9831-6

Faisal, A.-A., Kafy, A.-A., al Rakib, A., Akter, K. S., Jahir, D. M. A., Sikdar, M. S., Ashrafi, T. J., Mallik, S., & Rahman, M. M. (2021). Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area. Environmental Challenges, 4. https://doi.org/10.1016/j.envc.2021.100192

Feng, L., Zhao, M., Zhou, Y., Zhu, L., & Tian, H. (2020). The seasonal and annual impacts of landscape patterns on the urban thermal comfort using Landsat. Ecological Indicators, 110. https://doi.org/10.1016/j.ecolind.2019.105798

Gumma, M. K., Thenkabail, P. S., Teluguntla, P. G., Oliphant, A., Xiong, J., Giri, C., Pyla, V., Dixit, S., & Whitbread, A. M. (2020). Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud. GIScience and Remote Sensing, 57(3), 302–322. https://doi.org/10.1080/15481603.2019.1690780

Gunawardena, K. R., Wells, M. J., & Kershaw, T. (2017). Utilising green and bluespace to mitigate urban heat island intensity. Science of the Total Environment, 584–585, 1040–1055. https://doi.org/10.1016/j.scitotenv.2017.01.158

Hamblin, A. L., Youngsteadt, E., López-Uribe, M. M., & Frank, S. D. (2017). Physiological thermal limits predict differential responses of bees to urban heat-island effects. Biology Letters, 13(6). https://doi.org/10.1098/rsbl.2017.0125

Hauschild, T., & Jentschel, M. (2001). Comparison of maximum likelihood estimation and chi-square statistics applied to counting experiments. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 457(1–2), 384–401. https://doi.org/10.1016/S0168-9002(00)00756-7

Heimhuber, V., Tulbure, M. G., & Broich, M. (2018). Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics. Remote Sensing of Environment, 211, 307–320. https://doi.org/10.1016/j.rse.2018.04.016

Hussein, K., Alkaabi, K., Ghebreyesus, D., Liaqat, M. U., & Sharif, H. O. (2020). Land use/land cover change along the Eastern Coast of the UAE and its impact on flooding risk. Geomatics, Natural Hazards and Risk, 11(1), 112–130. https://doi.org/10.1080/19475705.2019.1707718

Imran, H. M., Hossain, A., Shammas, M. I., Das, M. K., Islam, M. R., Rahman, K., & Almazroui, M. (2022). Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh. Geomatics, Natural Hazards and Risk, 13(1), 2283–2312. https://doi.org/10.1080/19475705.2022.2114384

Jamali, A. (2019). Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran. SN Applied Sciences, 1(11). https://doi.org/10.1007/s42452-019-1527-8

Kafy, A.-A., Faisal, A.-A., Rahman, M. S., Islam, M., al Rakib, A., Islam, M. A., Khan, M. H. H., Sikdar, M. S., Sarker, M. H. S., Mawa, J., Mawa, J., & Sattar, G. S. (2021). Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh. Sustainable Cities and Society, 64. https://doi.org/10.1016/j.scs.2020.102542

Kafy, A.-A., Naim, M. N. H., Subramanyam, G., Faisal, A.-A., Ahmed, N. U., Rakib, A. A., Kona, M. A., & Sattar, G. S. (2021). Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh. Environmental Challenges, 4. https://doi.org/10.1016/j.envc.2021.100084

Kafy, A.-A., Saha, M., Faisal, A.-A., Rahaman, Z. A., Rahman, M. T., Liu, D., Fattah, M. A., al Rakib, A., AlDousari, A. E., Rahaman, S. N., Hasan, M. Z., & Ahasan, M. A. K. (2022). Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms. Building and Environment, 217. https://doi.org/10.1016/j.buildenv.2022.109066

Kusumawardani, K. P., & Hidayati, I. N. (2022). Analysis of urban heat island and urban ecological quality based on remote sensing imagery transformation in semarang city. IOP Conference Series: Earth and Environmental Science, 1089(1). https://doi.org/10.1088/1755-1315/1089/1/012037

Li, X., Chen, W., Cheng, X., & Wang, L. (2016). A comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery. Remote Sensing, 8(6). https://doi.org/10.3390/rs8060514

Li, Y., & Zhao, X. (2012). An empirical study of the impact of human activity on long-term temperature change in China: A perspective from energy consumption. Journal of Geophysical Research Atmospheres, 117(17). https://doi.org/10.1029/2012JD018132

Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277–293. https://doi.org/10.1016/j.isprsjprs.2017.06.001

Mansour, S., Al-Belushi, M., & Al-Awadhi, T. (2020). Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91. https://doi.org/10.1016/j.landusepol.2019.104414

Mondal, M. S., Sharma, N., Garg, P. K., & Kappas, M. (2016). Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egyptian Journal of Remote Sensing and Space Science, 19(2), 259–272. https://doi.org/10.1016/j.ejrs.2016.08.001

Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

Naim, M. N. H., & Kafy, A.-A. (2021). Assessment of urban thermal field variance index and defining the relationship between land cover and surface temperature in Chattogram city: A remote sensing and statistical approach. Environmental Challenges, 4. https://doi.org/10.1016/j.envc.2021.100107

Nugroho, N. Y., Triyadi, S., & Wonorahardjo, S. (2022). Effect of high-rise buildings on the surrounding thermal environment. Building and Environment, 207. https://doi.org/10.1016/j.buildenv.2021.108393

Onilude, O. O., & Vaz, E. (2021). Urban Sprawl and Growth Prediction for Lagos Using GlobeLand30 Data and Cellular Automata Model. Sci, 3(2). https://doi.org/10.3390/sci3020023

Pontius Jr., R. G., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. https://doi.org/10.1080/01431161.2011.552923

Ren, Z., Fu, Y., Dong, Y., Zhang, P., & He, X. (2022). Rapid urbanization and climate change significantly contribute to worsening urban human thermal comfort: A national 183-city, 26-year study in China. Urban Climate, 43. https://doi.org/10.1016/j.uclim.2022.101154

Saha, M., Kafy, A.-A., Bakshi, A., Faisal, A.-A., Almulhim, A. I., Rahaman, Z. A., al Rakib, A., Fattah, M. A., Akter, K. S., Rahman, M. T., Zhang, M., & Rathi, R. (2022). Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms. Energy and Buildings, 275. https://doi.org/10.1016/j.enbuild.2022.112452

Sailor, D. J. (2011). A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment. International Journal of Climatology, 31(2), 189–199. https://doi.org/10.1002/joc.2106

Sánchez-Espinosa, A., & Schröder, C. (2019). Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus landsat 8. Journal of Environmental Management, 247, 484–498. https://doi.org/10.1016/j.jenvman.2019.06.084

Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability (Switzerland), 11(11). https://doi.org/10.3390/su11113024

Shastri, S., Singh, P., Verma, P., Kumar Rai, P., & Singh, A. P. (2020). Land cover change dynamics and their impacts on thermal environment of Dadri block, Gautam budh Nagar, India. Journal of Landscape Ecology(Czech Republic), 13(2), 1–13. https://doi.org/10.2478/jlecol-2020-0007

Singh, P., Kikon, N., & Verma, P. (2017). Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustainable Cities and Society, 32, 100–114. https://doi.org/10.1016/j.scs.2017.02.018

Solecki, W. D., Rosenzweig, C., Parshall, L., Pope, G., Clark, M., Cox, J., & Wiencke, M. (2005). Mitigation of the heat island effect in urban New Jersey. Environmental Hazards, 6(1), 39–49. https://doi.org/10.1016/j.hazards.2004.12.002

Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations-A review. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071135

Uddin, S., al Ghadban, A. N., al Dousari, A., al Murad, M., & al Shamroukh, D. (2010). A remote sensing classification for land-cover changes and micro-climate in Kuwait. International Journal of Sustainable Development and Planning, 5(4), 367–377. https://doi.org/10.2495/SDP-V5-N4-367-377

Ullah, S., Tahir, A. A., Akbar, T. A., Hassan, Q. K., Dewan, A., Khan, A. J., & Khan, M. (2019). Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Sustainability (Switzerland), 11(19). https://doi.org/10.3390/su11195492

Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based Markov Chain model. Sustainability (Switzerland), 13(2), 1–22. https://doi.org/10.3390/su13020471

Wang, M., Zhang, Z., Hu, T., Wang, G., He, G., Zhang, Z., Li, H., Wu, Z., & Liu, X. (2020). An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4689–4701. https://doi.org/10.1109/JSTARS.2020.3014586

Yeneneh, N., Elias, E., & Feyisa, G. L. (2022). Detection of land use/land cover and land surface temperature change in the Suha Watershed, North-Western highlands of Ethiopia. Environmental Challenges, 7. https://doi.org/10.1016/j.envc.2022.100523

Zhang, Z., He, G., Wang, M., Long, T., Wang, G., Zhang, X., & Jiao, W. (2016). Towards an operational method for land surface temperature retrieval from Landsat 8 data. Remote Sensing Letters, 7(3), 279–288. https://doi.org/10.1080/2150704X.2015.1130877

Zhou, W., Qian, Y., Li, X., Li, W., & Han, L. (2014). Relationships between land cover and the surface urban heat island: Seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecology, 29(1), 153–167. https://doi.org/10.1007/s10980-013-9950-5