Prediction of Ground Water Level using Machine Learning
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 9
Abstract
Groundwater is a vital natural resource for various sectors including agriculture, industry, and domestic use. Timely and accurate prediction of groundwater levels plays a crucial role in effective water resource management and planning. In recent years, machine learning (ML) techniques have emerged as promising tools for forecasting groundwater levels due to their ability to capture complex relationships within hydrological systems. This study presents a comprehensive review and comparative analysis of ML-based models for predicting groundwater levels. First, we provide an overview of traditional methods employed in groundwater level prediction and discuss their limitations, highlighting the need for ML approaches. Subsequently, we delve into the application of various ML algorithms including support vector machines, random forests and ensemble methods for groundwater level prediction. We analyse the strengths and weaknesses of each algorithm in capturing temporal and spatial patterns of groundwater dynamics. Furthermore, we examine the influence of different input variables such as meteorological data, soil characteristics, and groundwater abstraction rates on the performance of ML models. The significance of feature selection and dimensionality reduction techniques in enhancing prediction accuracy is also discussed.
Authors and Affiliations
S. Nagavli, S. Dinesh Sai, P. Charan, P. Sathwik
Keywords
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- EP ID EP747880
- DOI https://doi.org/10.46501/IJMTST1009001
- Views 2
- Downloads 0
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