{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:47:04Z","timestamp":1792784824046,"version":"4.5.0"},"reference-count":28,"publisher":"American Chemical Society (ACS)","issue":"36","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"the 90th Anniversary of Chulalongkorn University Fund","id":[{"id":"https:\/\/ror.org\/028wp3y58","id-type":"ROR","asserted-by":"crossref"}]},{"name":"the Thailand Science Research and Innovation Fund","award":["-"],"award-info":[{"award-number":["-"]}],"id":[{"id":"https:\/\/ror.org\/000025p04","id-type":"ROR","asserted-by":"crossref"}]},{"name":"the Department of Groundwater Resources (DGR)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Groundwater is a critical resource supporting economic and social development in Thailand, particularly within the lower Chao Phraya Basin, where rapid urbanization and industrial expansion have led to intensive groundwater exploitation. This study applied a random forest (RF) machine learning model to investigate groundwater level (GWL) dynamics in the Nakhon Luang aquifer, which is one of the most productive aquifers due to its high yield and relatively good water quality, using long-term data from 1978 to 2023. The seven data sets include groundwater levels, groundwater pumping, rainfall, groundwater recharge, ground surface elevation, geological information, and lag-time features incorporated to represent delayed aquifer responses. The results demonstrate strong predictive performance, with R2 = 0.851 for the testing data set. Cross-validation results further indicated stable model performance (R2 = 0.833 \u00b1 0.018). Feature importance analysis revealed that groundwater pumping and recharge lag variables were the most influential factors controlling groundwater level variations. In addition, residual analysis and spatial error mapping were conducted to evaluate model uncertainty, while SHAP analysis was used to interpret the influence of input variables on groundwater level predictions. This study provides new insights into the dynamics of confined aquifer systems using machine-learning techniques and offers valuable information to support sustainable groundwater management in the study area.<\/jats:p>","DOI":"10.1021\/acsomega.6c04002","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T04:31:07Z","timestamp":1788409867000},"page":"54139-54152","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based\nAssessment of Long-Term Groundwater\nLevel Dynamics in the Nakhon Luang Aquifer from 1978 to 2023"],"prefix":"10.1021","volume":"11","author":[{"given":"Kanyapak","family":"Jankate","sequence":"first","affiliation":[{"name":"Chulalongkorn University , , ,","place":["Bangkok, Thailand, 10330"]},{"name":"Ministry of Natural Resources and Environment , , ,","place":["Bangkok, Thailand, 10900"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2241-9115","authenticated-orcid":true,"given":"Srilert","family":"Chotpantarat","sequence":"additional","affiliation":[{"name":"Chulalongkorn University , , ,","place":["Bangkok, Thailand, 10330"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"2026091510083388900_cit1","doi-asserted-by":"publisher","first-page":"140715","DOI":"10.1016\/j.jclepro.2024.140715","article-title":"Applications\nof machine learning to water resources management: A review of present\nstatus and future opportunities","volume":"441","author":"Ahmed","year":"2024","journal-title":"J. Cleaner\nProd."},{"key":"2026091510083388900_cit2","unstructured":"Department of Groundwater Resources.\n          .  Background\nof the Study Area. In Study and\nassessment of safe groundwater yield in critical groundwater zones\n(Phase 1); Department of Groundwater Resources:\nBangkok, 2022; pp 44\u201346, 44,45, 15\u201316."},{"key":"2026091510083388900_cit3","unstructured":"Department of Groundwater Resource.\n          . Groundwater Situation in Central Thailand.\nIn the National groundwater level and groundwater quality monitoring\nsystem project, Department of Groundwater Resources; Bangkok, 2022; Vol. 7, pp 34\u201345."},{"key":"2026091510083388900_cit4","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/B978-0-12-803166-7.00011-8","article-title":"Chapter 11 Groundwater Environment\nin Bangkok and the Surrounding Vicinity, Thailand","author":"Lorphensri","year":"2016","journal-title":"Groundwater Environment in Asian Cities: Concepts, Methods\nand Case Studies"},{"key":"2026091510083388900_cit5","unstructured":"Department of Groundwater Resources. Groundwater\nMonitoring Well\nNetwork.  In the groundwater situation in Bangkok\nand its vicinity, Department of Groundwater Resources; Bangkok, 2012. pp 7."},{"key":"2026091510083388900_cit6","doi-asserted-by":"publisher","first-page":"100303","DOI":"10.1016\/j.acags.2025.100303","article-title":"A systematic\nreview of machine learning\nmodels for groundwater level prediction","volume":"28","author":"Jesse","year":"2025","journal-title":"Applied\nComputing and Geosciences"},{"key":"2026091510083388900_cit7","doi-asserted-by":"publisher","first-page":"104244","DOI":"10.1016\/j.jafrearsci.2021.104244","article-title":"Comparing\nfour machine learning model performances in forecasting the alluvial\naquifer level in a semi-arid region","volume":"181","author":"El Bilali","year":"2021","journal-title":"J. Afr.\nEarth Sci."},{"key":"2026091510083388900_cit8","doi-asserted-by":"publisher","first-page":"130359","DOI":"10.1016\/j.jhydrol.2023.130359","article-title":"Artificial intelligence\nmodels to evaluate the impact of climate change on groundwater resources","volume":"627","author":"Secci","year":"2023","journal-title":"J. Hydrol."},{"key":"2026091510083388900_cit9","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"2026091510083388900_cit10","doi-asserted-by":"publisher","first-page":"109591","DOI":"10.1016\/j.ecolind.2022.109591","article-title":"Mapping\ngroundwater potential zones in Kanchanaburi Province, Thailand by\nintegrating of analytic hierarchy process, frequency ratio, and random\nforest","volume":"145","author":"Thanh","year":"2022","journal-title":"Ecol. Indic."},{"key":"2026091510083388900_cit11","unstructured":"Department of Mineral Resources. Geology.\n          .  Geological and Mineral Resources\nManagement Zoning: Bangkok Metropolitan Area; Department of Mineral Resources: Bangkok, 2016; pp 10\u201315."},{"key":"2026091510083388900_cit12","unstructured":"Japan International Cooperation Agency.\n          . The study area. In The Study on Management of Groundwater and Land Subsidence\nin the Bangkok Metropolis Area and Its Vicinity; Department of Mineral Resources: Bangkok, 1995; pp 37\u201344."},{"key":"2026091510083388900_cit13","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s13201-026-02873-3","article-title":"Background\nchloride concentrations in the Bangkok (BK),\nPhra Pradaeng (PD), and Nakhon Luang (NL) aquifers of Bangkok and\nsurrounding vicinity, Thailand","volume":"16","author":"Limpakanwech","year":"2026","journal-title":"Appl. Water\nSci."},{"key":"2026091510083388900_cit14","unstructured":"Department of Groundwater Resources. Conceptual Groundwater Model.\n          Development\nof a Geographic Information System for Groundwater Management in Critical\nGroundwater Areas; Department of Groundwater\nResources: Bangkok, 2012; pp 53\u201357."},{"key":"2026091510083388900_cit15","unstructured":"Kobkiat, S.\n          \n          Installation and\nSetup. In  Learning Data Science and AI: Machine\nLearning with Python. Media Network. Media\nNetwork Communication, Bangkok, Thailand, 2020; pp 34\u201372, 321\u2013336."},{"key":"2026091510083388900_cit16","doi-asserted-by":"publisher","first-page":"107629","DOI":"10.1016\/j.catena.2023.107629","article-title":"Active learning-based random forest\nalgorithm used for soil texture classification mapping in Central\nVietnam","volume":"234","author":"Ngu","year":"2024","journal-title":"Catena"},{"key":"2026091510083388900_cit17","unstructured":"Wankan, W.; Hencharoenlert, N.; Romsaiyud, W.\n          A predictive system\nto support decision-making on further education after junior high\nschool using the Random Forest algorithm. In  Proceedings of the 22nd National Graduate Research Conference of\nthe Northern Rajabhat University Network, Phitsanulok, Thailand, 2022."},{"key":"2026091510083388900_cit18","doi-asserted-by":"crossref","unstructured":"Shepard, D.\n          \n          A\ntwo-dimensional\ninterpolation function for irregularly spaced data.  Proceedings of the ACM National Conference; ACM Press1968, 517\u2013524.","DOI":"10.1145\/800186.810616"},{"key":"2026091510083388900_cit19","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.envsoft.2013.12.008","article-title":"A review of spatial\ninterpolation methods for environmental\nscientists","volume":"53","author":"Li","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"2026091510083388900_cit20","doi-asserted-by":"publisher","first-page":"102916","DOI":"10.1016\/j.mex.2024.102916","article-title":"Optimal interpolation\napproach for groundwater depth estimation","volume":"13","author":"Yasin","year":"2024","journal-title":"MethodsX"},{"key":"2026091510083388900_cit21","doi-asserted-by":"publisher","first-page":"126800","DOI":"10.1016\/j.jhydrol.2021.126800","article-title":"Long short-term memory\nneural network\n(LSTM-NN) for aquifer level time series forecasting using in-situ\npiezometric observations","volume":"601","author":"Solgi","year":"2021","journal-title":"J. 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Long Beach,\nCA, USA."},{"key":"2026091510083388900_cit26","unstructured":"Molnar, C.\n          \n          Model-Agnostic\nMethods. In  Interpretable Machine Learning:\nA Guide for Making Black Box Models Explainable, 2022; pp 177\u2013188."},{"key":"2026091510083388900_cit27","doi-asserted-by":"publisher","first-page":"100148","DOI":"10.1016\/j.aiig.2025.100148","article-title":"Prediction of groundwater level in Indonesian tropical\npeatland forest plantations using machine learning","volume":"6","author":"Yonekura","year":"2025","journal-title":"Artificial Intelligence in Geosciences"},{"key":"2026091510083388900_cit28","doi-asserted-by":"publisher","first-page":"148370","DOI":"10.1016\/j.scitotenv.2021.148370","article-title":"Climate and land-use change impacts on spatiotemporal\nvariations in groundwater recharge: A case study of the Bangkok Area,\nThailand","volume":"792","author":"Ghimire","year":"2021","journal-title":"Sci. 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