Abstract
Groundwater plays a crucial role in environment and living beings. It maintains the water level and base flow of rivers, lakes and wetlands especially during the dried months. However, current rates of over exploitation, pollution and mismanagement of groundwater pose a great threat to our ecosystem. A crucial aspect in the hydrological analysis is the fluctuation in Groundwater level (GWL), which is affected by numerous factors such as increases in water demand, faulty irrigation practices, mismanagement of soil, and uncontrolled exploitation of aquifers, etc. Traditionally, methods like measuring the water level in a shallow well with tape and surface geophysical methods are mostly used, but they are often time-consuming, costly, and also database dependent. Keeping the groundwater dynamics in mind there is a need to develop a reliable and accurate method for GWL forecast and to meet these challenges, Machine Learning (ML) and Artificial Intelligence (AI) techniques are currently in huge demand. Although a suitable selection of arithmetic or stochastic model building approaches could simulate the discontinuity and complex dynamics of groundwater but they have challenging aspects and require a bit of experience. Therefore various ML and AI techniques and their corresponding methodologies which are mostly used to simulate and predict the GWL alterations have been discussed in this review paper (2011–2020).
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Saha, S., Mallik, S., Mishra, U. (2022). Groundwater Depth Forecasting Using Machine Learning and Artificial Intelligence Techniques: A Survey of the Literature. In: Das, B.B., Hettiarachchi, H., Sahu, P.K., Nanda, S. (eds) Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—GEO-TRA-ENV-WRM. Lecture Notes in Civil Engineering, vol 207. Springer, Singapore. https://doi.org/10.1007/978-981-16-7509-6_13
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