Deep learning for Multi-horizon Water levelForecasting in KRS reservoir, India

Abstract

In recent times, the densely populated Bengaluru metropolis in India has faced challenges related to water scarcity, particularly relying on the Krishna Raja Sagara (KRS) dam. The forecasting of reservoir water levels has become challenging due to spatio-temporal fluctuations in meteorological conditions and complex physical processes. As a result, developing suitable water management to meet the population's water demand requires an accurate and dependable estimate of the dam's water level. This work attempted to use a daily reservoir and weather data by utilizing long short-term memory (LSTM) networks. Seven high-performance models (viz., M1 to M7) with varying window sizes and horizons have been trained on this data and their performance is compared. The performance metrics revealed that the M7 model outperformed the other models for reservoir water level prediction, with coefficient of determination (R2) score of 0.93, root mean square error (RMSE) of 2.94, and mean absolute percentage error (MAPE) of 0.01. This study also provides a valuable dashboard for tracking forecasted water levels in the largest reservoir in the Cauvery basin. Finally, the innovative integration of LSTM technology in water level predictions for the Krishna Raja Sagara (KRS) dam not only addresses the challenges posed by spatio-temporal fluctuations but also sets a new standard for precision in forecasting, thereby establishing a crucial decision-support tool for real-time monitoring and enhanced water resource management in India's Bengaluru metropolis.

Keywords

• Reservoir water level
• Multi-horizon forecasting
• Deep learning
• Hydrological modelling
• LSTM