Enhancing Groundwater Level Forecasting Using Historical Data and Machine Learning Techniques

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@article{1717877,
      author = {Abhi Modi, Dr. S. Balamurugan},
      title = {Enhancing Groundwater Level Forecasting Using Historical Data and Machine Learning Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2312-2321},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717877.pdf},
      abstract = {Groundwater is an essential source of fresh water which is used in agriculture, domestic consumption, and industries. Over-exploitation, climate changes, and population increase are reducing the level of groundwater in most areas. Proper ground water levels forecasting would be therefore critical in proper water management and sustainability. The latest researches have utilized machine and deep learning to forecast the groundwater level by making use of past hydrological and climatic records. Random Forest, Support Vector Machines, Artificial Neural Networks, and Long Short-Term Memory (LSTM) algorithms have demonstrated promising outcomes in ground water on capturing complex trends in groundwater datasets. Others have also combined satellite data and environmental variables to enhance the accuracy of prediction. Nonetheless, a number of current studies are area-specific, use smaller datasets, or models of individual machine learning. This complicates the situation of establishing the most effective methods of forecasting groundwater over time under comparable circumstances. In order to solve such a problem, the paper offers a machine learning-based model of improving groundwater level predictions on the basis of historical groundwater and environmental data. The approach will involve processing of data, selection of features, and applying various machine learning models to compare them. The standard evaluation metrics to be used in evaluating the performance of these models will be accuracy, RMSE, and R 2. It is hoped that, the anticipated value of this study would be identification of relevant machine learning methods to predict groundwater, and offer a systematized framework that could be used to provide a sustainable groundwater management and further research in hydrology.},
      keywords = {Groundwater Level Prediction, Machine Learning, Time-Series Forecasting, Hydrological Data Analysis, Random Forest, Long Short-Term Memory (LSTM) Water Resource Management.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717877}
  }