Mahzabin, A., Hossain, M. J. & Alam, S. Groundwater level depletion assessment of Dhaka city using MODFLOW. Am. J. Water Resour. 11(1), 28–40. https://doi.org/10.12691/ajwr-11-1-4 (2023).
Ibrahim-Bathis, K. & Ahmed, S. A. Geospatial technology for delineating groundwater potential zones in Doddahalla watershed of Chitradurga district, India. Egypt. J. Remote Sens. Sp. Sci. 19, 223–234 (2016).
Haghighi, A. T. et al. Unsustainability syndrome—from meteorological to agricultural drought in arid and semi-arid regions. Water 12(3), 838 (2020).
Arulbalaji, P., Padmalal, D. & Sreelash, K. GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern western Ghats. India. Sci. Rep. 9, 1–17 (2019).
Konkul, J., Rojborwornwittaya, W. & Chotpantarat, S. Hydrogeologic characteristics and groundwater potentiality mapping using potential surface analysis in the Huay Sai area Phetchaburi Province, Thailand. Geosci. J. 18, 89–103 (2014).
Sarkar, S. K., Esraz-Ul-Zannat, M., Das, P. C. & Ekram, K. M. Delineating the groundwater potential zones in Bangladesh. Water Supply 22(4), 4500–4516. https://doi.org/10.2166/ws.2022.113 (2022).
Karunanidhi, D., Aravinthasamy, P., Deepali, M., Subramani, T. & Shankar, K. Groundwater pollution and human health risks in an industrialized region of southern India: Impacts of the COVID-19 lockdown and the monsoon seasonal cycles. Arch. Environ. Contam. Toxicol. 80, 259–276 (2021).
Qasemi, M. et al. Cadmium in groundwater consumed in the rural areas of Gonabad and Bajestan, Iran: Occurrence and health risk assessment. Biol. Trace Elem. Res. 192, 106–115 (2019).
Aravinthasamy, P., Karunanidhi, D., Subramani, T. & Roy, P. D. Demarcation of groundwater quality domains using GIS for best agricultural practices in the drought-prone Shanmuganadhi river basin of south India. Environ. Sci. Pollut. Res. 28, 18423–18435 (2021).
Mishra, S., Chauhan, M. S. & Sundaramurthy, S. Assessment of groundwater trends in Bhopal Madhya Pradesh: A statistical approach. Sustainability 15, 1–15 (2023).
Sarkar, S. K. et al. Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management. Front. Eng. Built Environ. 2(1), 43–54. https://doi.org/10.1108/FEBE-09-2021-0044 (2021).
Priya, U. et al. Sustainable groundwater potential zoning with integrating GIS, remote sensing, and AHP model: A case from north-central Bangladesh. Sustain 14, 5640 (2022).
Roy, P. K. et al. Modelling groundwater potential zone using fuzzy logic and geospatial technology of an deltaic island. Model. Earth Syst. Environ. 8, 5565–5584 (2022).
Bhadran, A. et al. A GIS based Fuzzy-AHP for delineating groundwater potential zones in tropical river basin, southern part of India. Geosyst. Geoenviron. 1, 100093 (2022).
Singha, C. et al. Mapping groundwater potential zone in the Subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine. Heliyon 10, e24308 (2024).
Maity, B., Mallick, S. K., Das, P. & Rudra, S. Comparative analysis of groundwater potentiality zone using fuzzy AHP, frequency ratio and Bayesian weights of evidence methods. Appl. Water Sci. 12, 1–16 (2022).
Radhakrishnan, R. & CA, L. D. Groundwater level prediction using support vector machine and M5 model tree—a case study. SSRN Electron. J. https://doi.org/10.2139/ssrn.4512253 (2023).
Gómez-Escalonilla, V., Martínez-Santos, P. & Martín-Loeches, M. Preprocessing approaches in machine-learning-based groundwater potential mapping: An application to the Koulikoro and Bamako regions. Mali. Hydrol. Earth Syst. Sci. 26, 221–243 (2022).
Yadav, B., Gupta, P. K., Patidar, N. & Himanshu, S. K. Ensemble modelling framework for groundwater level prediction in urban areas of India. Sci. Total Environ. 712, 1–36 (2020).
Rahman, M. Ground water level prediction using artificial neural network. Int. J. Hydrol. Sci. Technol. 6(4), 371–381. https://doi.org/10.1504/IJHST.2016.079356 (2016).
Al-Waeli, L. K., Sahib, J. H. & Abbas, H. A. ANN-based model to predict groundwater salinity: A case study of West Najaf-Kerbala region. Open Eng. 12(1), 120–128 (2022).
Beheshtirad, M. Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed Iran. Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang. Geocarto Int. https://doi.org/10.1080/10106049.2014.966161 (2021).
Lee, S. & Lee, C. Application of decision-tree model to groundwater productivity-potential mapping. Sustainability https://doi.org/10.3390/su71013416 (2015).
Rahmati, O., Pourghasemi, H. R. & Melesse, A. M. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran region. Iran. Catena 137, 360–372 (2016).
Wahile, B. et al. Regional Studies Integration of shannon entropy (SE), frequency ratio (FR) and analytical hierarchy process (AHP) in GIS for suitable groundwater potential zones targeting in the Yoyo river basin, Meiganga area. J. Hydrol. 39. https://doi.org/10.1016/j.ejrh.2022.100997 (2022).
Raisa, S. S., Sarkar, S. K. & Sadiq, M. A. Advancing groundwater vulnerability assessment in Bangladesh: a comprehensive machine learning approach. Groundw. Sustain. Dev. 25, 101128. https://doi.org/10.1016/j.gsd.2024.101128 (2019).
Wunsch, A., Liesch, T. & Broda, S. Deep learning shows declining groundwater levels in Germany until 2100 due to climate change. Nat. Commun. 13, 1–13 (2022).
Iftikhar, S., Bhatti, S., Memon, M. A. & Bhatti, Z. A. Groundwater arsenic and health risk prediction model using machine learning for T.M Khan Sindh Pakistan. Int. J. Inf. Technol. Comput. Sci. 12, 24–31 (2020).
Rasool, U. et al. Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan Pakistan. Chemosphere 303, 135265 (2022).
Karunasiri, W., Perera, N. & Sirisena, K. Ai-based machine learning algorithms for water quality analysis : A review. ICSBE 2023-268, 13–15 (2023).
Bahmani, R., Solgi, A. & Ouarda, T. B. Groundwater level simulation using gene expression programming and M5 model tree combined with wavelet transform. Hydrol. Sci. J. 65(8), 1430–1442 (2020).
Mohammad-Azari, S., Bozorg-Haddad, O. & Loáiciga, H. A. State-of-art of genetic programming applications in water-resources systems analysis. Environ. Monit. Assess. 192, 1–7 (2020).
Ghazi, B., Jeihouni, E., Kouzehgar, K. & Torabi, A. Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet—GEP model. Environ. Earth Sci. 80, 1–15 (2021).
Sarkar, S. K. et al. Artificial neural network-based land use-specific carbon patterns and their effects on land surface temperature as a result of the Rohingya refugee influx. IEEE Access 11, 142964–142978 (2023).
Rudra, R. R., Sharif, M. S. & Mahi, M. M. Subsistence after resettlement: Observations from Gucchagram project in Narail district Bangladesh. Khulna Univ. Stud. 16, 321–331 (2022).
Mahi, M. M., Sharif, M. S., Rudra, R. R. & Haque, M. N. The geo-spatial approach to detect the change in vegetation and land surface temperature (Lst) after formation of Rohingya settlements in Bangladesh. J. Civ. Eng. Sci. Technol. 12, 288–241 (2021).
Roy, P., Ahmed, M. A. & Kumer, A. An Overview of hygiene practices and health risks related to street foods and drinking water from roadside restaurants of Khulna city of Bangladesh. Eur. J. Environ. Res. 3(2), 47–55 (2019).
Roy, P. et al. Water Supply, Sanitation system and water-borne diseases of Slum Dwellers of Bastuhara Colony, Khulna. In 5th International Conference on Civil Engineering for Sustainable Development (ICCESD 2020) 0–9 (2020).
Ahmed Khan, T., Brata Paul Argha, D. & Shirin Anita, M. An analysis of existing medical waste management and possible health hazards in Jhenaidah municipality. ICERIE 677–683 (2021).
Sarkar, S. K., Rudra, R. R., Nur, M. S. & Das, P. C. Partial least-squares regression for soil salinity mapping in Bangladesh. Ecol. Indic. 154, 110825 (2023).
Sarkar, S. K. et al. Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh. Environ. Develop. Sustain. https://doi.org/10.1007/s10668-024-04687-2 (2022).
Mahi, M. M., Sharif, M. S. & Rudra, R. R. Passenger travel behavior before & during the Covid-19 outbreak: A comparative analysis. Khulna Univ. Stud. 19, 368–381 (2022).
Sarkar, S. K., Rudra, R. R. & Santo, M. M. H. Cyclone vulnerability assessment in the coastal districts of Bangladesh. Heliyon 10, e23555 (2024).
Argha, D. B. & Ahmed, M. A. A machine learning approach to understand the impact of temperature and rainfall change on concrete pavement performance based on LTPP data. SVU-Int. J. Eng. Sci. Appl. 5(1), 150–5 (2024).
Brata, D., Argha, P. & Model, R. Design of photovoltaic system for green manufacturing by using statistical design of experiments. https://doi.org/10.20944/preprints202310.1913.v3 (2023).
Chowdhury, H. A smart circular economy for integrated organic hydroponicaquaponic farming. 31–41. https://doi.org/10.13140/RG.2.2.18930.27844 (2023).
Das, A. et al. Advancements in adsorption based carbon dioxide capture technologies—A comprehensive review. Heliyon 9, e22341 (2023).
Abedin, M. A., Collins, A. E., Habiba, U. & Shaw, R. Climate change, water scarcity, and health adaptation in southwestern coastal Bangladesh. Int. J. Disaster Risk Sci. 10, 28–42 (2019).
Hadi, T. An analysis of water policies and strategies of bangladesh in the context of climate change. Asia. Pac. J. Rural Dev. 29, 111–123 (2019).
Chowdhury, H. Semiconductor manufacturing process improvement using data-driven methodologies. Preprints, 2023100056. https://doi.org/10.20944/preprints202310.0056.v2 (2023).
Chowdhury, H. Human–robot collaboration in manufacturing assembly tasks. Preprints, 2023100049. https://doi.org/10.20944/preprints202310.0049.v2 (2023).
Haque, M. N., Mahi, M. M., Sharif, M. S., Rudra, R. R. & Sharifi, A. Changes in the economic value of ecosystem services in rapidly growing urban areas: the case of Dhaka. Bangladesh. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-023-26096-0 (2023).
Chowdhury, H. 127-Multiple charger with adjustable voltage using solar panel ICMERE2015- PI-221. (2020).
Fatema, K., Joy, A. R., Amin, F. M. R. & Sarkar, S. K. Groundwater potential mapping in Jashore, Bangladesh Heliyon groundwater potential mapping in Jashore. Bangladesh. Heliyon 9, e13966 (2023).
Glantz, M. H. Recycling of cotton dust for organic farming is a pivotal replacement of chemical fertilizers by composting and its quality analysis. Environ. Res. Technol. 5, 108–116 (2021).
Sarkar, S. K., Talukdar, S., Rahman, A. & Roy, S. K. Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management. Front. Eng. Built Environ. 2(1), 43–54 (2022).
Talukdar, S. et al. Novel hybrid models to enhance the efficiency of groundwater potentiality model. Appl. Water Sci. 12, 62 (2022).
Sarkar, S. K., Rahman, M. A., Esraz-Ul-zannat, M. & Islam, M. F. Simulation-based modeling of urban waterlogging in Khulna city. J. Water Clim. Chang. 12, 566–579 (2021).
Moghaddam, H. K., Moghaddam, H. K., Kivi, Z. R., Bahreinimotlagh, M. & Alizadeh, M. J. Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundw. Sustain. Dev. 9, 100237 (2019).
Fullér, R. Artificial neural networks. (2000).
Chowdhury, H., Brata, D., Argha, P. & Ahmed, A. Artificial intelligence in sustainable vertical farming. Comput. Soc. https://doi.org/10.48550/arXiv.2312.00030 (2023).
Sarkar, S. K. et al. Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh. Sci. Rep. 13, 1–16 (2023).
Nevtipilova, V. Testing artificial neural network (ANN) for spatial interpolation. J. Geol. Geosci. 03, 1–9 (2014).
Volná, E. Neuronové Sítě 1. Ostravska univerzita v Ostrave (2008).
Rudra, R. R. & Sarkar, S. K. Artificial neural network for flood susceptibility mapping in Bangladesh Heliyon artificial neural network for flood susceptibility mapping in Bangladesh. Heliyon 9, e16459 (2023).
Tarassenko, L. A guide to neural computing applications 1st Edition, 8–10, (Elsevier, 1998).
Tu, J. V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49, 1225–1231 (1996).
Chowdhury, H. Circular economy integration in additive manufacturing. Preprints, Version 1, 2023100087. https://doi.org/10.20944/preprints202310.0087.v1 (2023).
Ayalew, L. & Yamagishi, H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Central Japan. Geomorphol 65, 15–31 (2005).
Pradhan, B. & Lee, S. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ. Earth Sci. 60, 1037–1054 (2010).
Krhoda, G. O. & Amimo, O. M. Groundwater quality prediction using logistic regression model for Garissa county. Afr. J. Phys. Sci. 3, 13–27 (2019).
Poirot, H. Logistic regression. Speech and Language Processing, Chapter 5. https://web.stanford.edu/~jurafsky/slp3/5.pdf (2023).
Khurram, F. B., Johora, F. T., Meem, T. M. & Khan, M. S. Association between nutritional status and mental health among adults during Covid-19 pandemic in Khulna city corporation. Khulna Univ. Stud. 16, 352–67 (2022).
Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B. & Revhaug, I. Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13, 361–78 (2016).
Landwehr, N., Hall, M. & Frank, E. Logistic model trees. Mach. Learn. 59, 161–205 (2005).
Ghasemkhani, B., Yilmaz, R., Birant, D. & Kut, R. A. Logistic model tree forest for steel plates faults prediction. Machines 11, 679 (2023).
Fayaz, S. A., Zaman, M. & Butt, M. A. An application of logistic model tree (LMT) algorithm to ameliorate prediction accuracy of meteorological data. Int. J. Adv. Technol. Eng. Explor. 8, 1424–1440 (2021).
Mallick, J. et al. Developing a new method for future groundwater potentiality mapping under climate change in Bisha watershed. Saudi Arabia. Geocarto Int. 37, 14495–14527 (2022).
Ghats, W., Nair, H. C., Padmalal, D., Joseph, A. & Vinod, P. G. Delineation of groundwater potential zones in river basins using geospatial tools—an example from southern western Ghats, Kerala, India. J. Geovisualization Spat. Anal. 1, 1–6. https://doi.org/10.1007/s41651-017-0003-5 (2017).
Yeh, H., Cheng, Y., Lin, H. & Lee, C. Mapping groundwater recharge potential zone using a GIS approach in Hualian river. Taiwan. Sustain. Environ. Res. 26, 33–43 (2016).
Jannis, E., Adrien, M., Annette, A. & Peter, H. Climate change effects on groundwater recharge and temperatures in Swiss alluvial aquifers. J. Hydrol. X 11, 100071 (2021).
Elmahdy, S., Mohamed, M. & Ali, T. Land use/land cover changes impact on groundwater level and quality in the northern part of the United Arab Emirates. Remote Sens. 12, 1715 (2020).
Kaur, L., Rishi, M. S., Singh, G. & Nath, S. Groundwater potential assessment of an alluvial aquifer in Yamuna sub-basin (Panipat region) using remote sensing and GIS techniques in conjunction with analytical hierarchy process ( AHP ) and catastrophe theory. Ecol. Indic. 110, 105850 (2020).
De Reu, J. et al. Application of the topographic position index to heterogeneous landscapes. Geomorphology 186, 39–49 (2013).
Mokarram, M., Roshan, G. & Negahban, S. Landform classification using topography position index (case study: Salt dome of Korsia-Darab plain, Iran). Model. Earth Syst. Environ. 1, 1–7 (2015).
Ashraf, B., Aghakouchak, A., Alizadeh, A. & Mou, M. Quantifying anthropogenic stress on groundwater. Resources. https://doi.org/10.1038/s41598-017-12877-4 (2017).
Arefin, R. Groundwater potential zone identification using an analytic hierarchy process in Dhaka City. Bangladesh. Environ. Earth Sci. 79, 1–16 (2020).
Ahmed, A. Groundwater potentiality mapping using machine learning algorithms BouSbaa area, Marrakech, Morocco. 1–21 (2023).
Mehedi, H., Mandal, H., Pravat, H. & Shit, K. Groundwater potential mapping using multi-criteria decision, bivariate statistic and machine learning algorithms: Evidence from Chota Nagpur Plateau. India. Appl. Water Sci. 12, 1–16 (2022).
Adiat, K. A., Ajayi, O. F., Akinlalu, A. A. & Tijani, I. B. Prediction of groundwater level in basement complex terrain using artificial neural network: a case of Ijebu-Jesa, southwestern Nigeria. Appl. Water Sci. 10, 1–4 (2020).
Porte, P., Isaac, R. K., Mahilang, K. K., Sonboier, K. & Minj, P. Groundwater level prediction using artificial neural network model. Int. J. Curr. Microbiol. Appl. Sci. 72, 2947–2954 (2018).
Nathan, N. S., Saravanane, R. & Sundararajan, T. Application of ANN and MLR models on groundwater quality using CWQI at Lawspet. Puducherry India. https://doi.org/10.4236/gep.2017.53008 (2017).
Schuman, C. D. & Birdwell, J. D. Dynamic artificial neural networks with affective systems. PLoS One 8, e80455 (2013).
Zhang, M., Zhang, C., Kafy, A.-A. & Tan, S. Simulating the relationship between land use/cover change and urban thermal environment using machine learning algorithms in Wuhan city, China. Land, 11, 14. https://doi.org/10.3390/land11010014 (2022).
Lee, J., Jung, C., Kim, S. & Kim, S. Assessment of climate change impact on future groundwater-level behavior using SWAT groundwater-consumption function in Geum river basin of South Korea. Water, 11, 949. https://doi.org/10.3390/w11050949 (2019).
Song, S.-H. & Choi, K.-J. An appropriate utilization of agricultural water resources of Jeju Island with climate change (I). J. Soil Groundw. Environ. 17(2), 62–70. https://doi.org/10.7857/JSGE.2012.17.2.062 (2012).
Parry, M. L. et al. Climate change 2007 Impacts, Adaptions And Vulnerability, 250–270. (IPCC Working Group II, Cambridge University Press, 2007).
Li, H. et al. Seasonal and inter-annual variability of groundwater and their responses to climate change and human activities in arid and desert areas: A case study in Yaoba Oasis, Northwest China. Water, 12, 303. https://doi.org/10.3390/w12010303 (2020).