Paper Title
Machine Learning Based Prediction of Water Level and Volume in Farm Wells for Sustainability

Water resources are crucial for agriculture, and wells located near agricultural fields are often used as water sources. However, there is significant temporal variability in water use patterns, which can make it difficult to predict future water availability. This study focuses on the analysis of water level and volume in wells located near agricultural fields, using water level and volume data collected over a one-year period. Daily water level and volume patterns were well described by autoregressive integrated moving average (ARIMA) models. Model development was supported by unsupervised clustering analysis that revealed similarities in water use patterns and confirmed the time-series water use model attributes. The inclusion of ambient temperature as a model attribute improved ARIMA model performance for daily water level and volume in a well. The study aims to provide insights into the behavior of the water system and enable better decision-making in agricultural operations that depend on this water source. The ARIMA model can help predict future water availability, which can be critical for planning and managing agricultural activities such as irrigation and crop selection. The study can contribute to more efficient and sustainable use of water resources in agriculture.