{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:01:22Z","timestamp":1790834482486,"version":"4.1.0"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Groundwater resources in Chennai, Tamil Nadu, India are under increased stress due to rapid urbanization, erratic monsoon patterns and largely unregulated groundwater extraction. Addressing this challenge requires not only continuous monitoring but also accurate forecasting of groundwater fluctuations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study presents a data driven framework that combines Internet of Things (IoT) enabled sensing with Machine Learning model. Real-time measurements such as groundwater depth, rainfall, temperature, and soil moisture are combined with historical hydro-meteorological data to build a reliable prediction system. A hybrid modelling approach is adopted in this study, where Random Forest is used to identify key influencing factors and capture non-linear relationships, while Long Short-Term Memory (LSTM) networks are employed to model temporal variations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The combined RF-LSTM model surpasses traditional techniques, delivering an RMSE of 0.38 m, an MAE of 0.29 m, and R\n                      <jats:sup>2<\/jats:sup>\n                      value of 0.96. Relative to a standalone LSTM model, the hybrid approach cuts the RMSE by approximately 22%, and it also outperforms Random Forest, Support Vector Regression, and Linear Regression individually.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The model successfully tracks seasonal shifts in groundwater capturing groundwater recharge during the Northeast monsoon and depletion during the summer season. Through timely and dependable forecasts, this system aids more effective groundwater management, strengthens rainwater harvesting efforts and supports sustainable urban water practices going forward.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1958933","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:41:44Z","timestamp":1790833304000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India"],"prefix":"10.3389","volume":"9","author":[{"given":"S.","family":"Boomika","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Sitharthan","sequence":"additional","affiliation":[{"name":"Centre for Smart Grid Technologies, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Tanukasree","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Senthil Kumar","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"949","DOI":"10.3390\/w14060949","article-title":"Groundwater level modeling with machine learning: a systematic review and meta-analysis","volume":"14","author":"Ahmadi","year":"2022","journal-title":"Water"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"6319","DOI":"10.3390\/su14106319","article-title":"Forecasting of streamflow and comparison of artificial intelligence methods: a case study for Meram stream in Konya, Turkey","volume":"14","author":"Asaad","year":"2022","journal-title":"Sustainability"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s41101-025-00477-7","article-title":"Spatio-temporal prediction of groundwater level and quality using IoT sensing and hybrid machine learning approaches","volume":"11","author":"Banerjee","year":"2026","journal-title":"Water Conserv. 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