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              <text>Prediction of ground water quality in western regions of Tamilnadu using LSTM network</text>
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              <text>Groundwater; LSTM; Prediction; Tamil Nadu; Western region</text>
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              <text>Assessing and safeguarding groundwater quality is critical for sustaining life in water-scarce regions like western Tamil Nadu. The motivation behind this study stems from the pressing need to address water quality challenges in a region grappling with scarcity. Despite existing efforts, a notable research gap exists in predictive tools that comprehensively capture the nuanced temporal variations and trends in groundwater quality. This is where the LSTM network steps in, showcasing exceptional accuracy in short-term predictions and discerning long-term trends. This research uses Long Short-Term Memory (LSTM) networks, a variant of recurrent neural networks, to predict groundwater quality in South Indian Regions, especially in Tamil Nadu. Extensive data, encompassing parameters such as pH, dissolved oxygen, turbidity, and various chemical constituents, were gathered over an extended timeframe. The LSTM model was then trained on this historical dataset, factoring in temporal dependencies and seasonality inherent in groundwater quality data. The validation process rigorously tests the LSTM model against actual groundwater quality measurements. The results were impressive, as the model demonstrated a remarkable ability to unravel the complex variations in groundwater quality.  2024 Elsevier B.V.</text>
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              <text>M K.; C V.S.R.P.; J V.A.; A S.; M S.; K.B P.; Shanmugm M.</text>
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              <text>Groundwater for Sustainable Development, Vol-25</text>
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              <text>Elsevier B.V.</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1016/j.gsd.2024.101156" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1016/j.gsd.2024.101156&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188555566&amp;amp;doi=10.1016%2Fj.gsd.2024.101156&amp;amp;partnerID=40&amp;amp;md5=1ad8807b4103a20417113b25393cc0b0" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188555566&amp;amp;doi=10.1016%2fj.gsd.2024.101156&amp;amp;partnerID=40&amp;amp;md5=1ad8807b4103a20417113b25393cc0b0&lt;/a&gt;</text>
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              <text>ISSN: 2352801X</text>
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              <text>M K., Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, India; C V.S.R.P., Department of Civil Engineering, Malla Reddy Engineering College, Telangana, Secunderabad, 500100, India; J V.A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; A S., Department of Science and Humanities, Brilliant Grammar School Educational Society's Group of Institutions Integrated Campus, Telangana, Hyderabad, 501505, India; M S., Technical Executive, National Agro Foundation, Tamil Nadu, Chennai, 600113, India; K.B P., Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, BSF Campus, Yelahanka, Govindapura, Karnataka, Bengaluru, 560064, India; Shanmugm M., Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, 638060, India</text>
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