In the battle against rising groundwater salinity, a silent but powerful ally has emerged: machine learning. A comprehensive review published in Discover Water, the English translation of the journal name, led by Dilip Kumar Roy of the Irrigation and Water Management Division at the Bangladesh Agricultural Research Institute, sheds light on the transformative potential of machine learning (ML) and deep learning (DL) in managing coastal aquifers. These vital water sources are under threat from saltwater intrusion, a process exacerbated by climate change and excessive groundwater extraction.
The review, which analyzed 104 peer-reviewed journal articles, reveals a growing trend in the use of ML and DL techniques to predict and manage groundwater salinity. Traditional methods, such as Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems, have paved the way for more advanced DL models like Convolutional Neural Networks and Long Short-Term Memory Networks. These models are not just academic curiosities; they are proving to be invaluable tools in the real world, offering more accurate predictions and better management strategies.
“Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings,” Roy explains. This reliance is not surprising, given the complexity of the task. Coastal aquifers are dynamic systems, influenced by a multitude of factors including rainfall, tides, and human activities. ML and DL models excel at handling this complexity, learning from vast amounts of data to identify patterns and make predictions that would be impossible for humans to discern.
The review highlights several key findings. Ensemble methods, which combine multiple models to enhance accuracy, are proving particularly effective. Models like Random Forest and Grasshopper Optimization Algorithm-XGBoost are leading the pack, offering top-tier performance. However, the review also underscores the importance of model selection, input variable ranking, and computational efficiency. These factors are crucial for ensuring that ML models are not just accurate, but also practical and applicable in real-world scenarios.
The implications for the energy sector are significant. Groundwater is a critical resource for energy production, from cooling power plants to generating geothermal energy. Rising salinity can corrode equipment, reduce efficiency, and increase maintenance costs. By providing more accurate predictions and better management strategies, ML and DL models can help mitigate these challenges, ensuring a more sustainable and cost-effective use of groundwater resources.
Looking ahead, the future of groundwater management in coastal aquifers is set to be redefined by these advanced techniques. The review suggests that future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches. These innovations could explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity more effectively.
As the threat of saltwater intrusion continues to grow, so too does the importance of these advanced modeling techniques. By harnessing the power of ML and DL, we can better understand and manage our groundwater resources, ensuring a more sustainable future for both ecosystems and the energy sector. The review, published in Discover Water, serves as a call to action, urging researchers and practitioners to embrace these innovative techniques and drive forward the field of groundwater management.
