Hybrid convolutional neural network and random forest model for predicting water level fluctuations in Kaptai reservoir to enhance water resource management

Abstract

The Kaptai Reservoir, the country's largest artificial freshwater body plays a vital role in hydroelectric power generation, flood control and agricultural support. However, its water levels are subject to fluctuations influenced by both climatic variations and increasing human consumption. Despite its crucial environmental impact there is a lack of research on the Kaptai Reservoir water body. The escalating effects of climate change present significant challenges to water resource management particularly in vulnerable regions such as Bangladesh. This paper presents a machine learning-based approach utilizing Convolutional Neural Networks (CNN) for feature extraction and Random Forest Regression (RFR) for Predicting water level fluctuations in the Kaptai Reservoir. Historical climate data from 2013 to 2022 including rainfall, temperature, and humidity were used to train the model. The CNN model effectively captured both temporal and spatial relationships in the water level time series while the RFR model demonstrated high prediction accuracy with a Root Mean Square Error (RMSE) of 0.5267 and a Mean Absolute Error (MAE) of 0.3128. These results highlight the model's strong performance and its potential for real-time water management and decision-making aimed at mitigating the impacts of climate change on the reservoir. The findings offer valuable insights for policymakers working to ensure the long-term sustainability and resilience of water resources in Bangladesh.