Abstract
Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future.
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Data Availability
The dataset generated and analyzed throughout this study can be obtained by contacting the corresponding author. Requests for access to the dataset will be considered on a reasonable basis to facilitate further research and collaboration.
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The authors acknowledged the KLS Gogte Institute of Technology and Visvesvaraya Technological University, Belagavi, Karnataka, India, for supporting the research work by providing the facilities.
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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.
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Sanu, S.G., Math, M.M. Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources. SN COMPUT. SCI. 5, 338 (2024). https://doi.org/10.1007/s42979-024-02686-8
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DOI: https://doi.org/10.1007/s42979-024-02686-8