Feasibility Study on Forecasting Water Level Fluctuation of the Senanayaka Samudra Reservoir

Abstract

The Senanayake Samudraya Reservoir in Ampara District of Sri Lanka is a crucial piece of water management infrastructure. It supports various sectors, including agriculture, industry, and domestic water needs. The country experiences variable rainfall patterns throughout the year due to its tropical climate and diverse topography. Catchment area of the reservoir is 994.22 sq.km and command area is 120,254 ac (488.66 sq.km). Data for the reservoir was collected from irrigation department, Ampara and 26-year (1990-2015) rainfall and temperature data collected from Department of Meteorology and Irrigation department. This study developed a comprehensive water level forecasting system for the Reservoir to improve water resources management system. Both Hydrologic Engineering Centre-Hydrologic Modelling System (HECHMS) and Soil Conservation Service-Curve number (SCSCN) methods were used to simulate the surface inflow and compared, with SCS-CN proving more reliable for this study. Then, the base flow of the reservoir was determined. The Long Short-Term Memory (LSTM) recurrent neural network (RNN) was employed to forecast water level fluctuations. The model performance was evaluated based on the computed statistical parameters. The performance of model is very good with Coefficient of Determination (R2) = 0.998, Mean Square Error (MSE)= 0.0357 and Root Mean Squared Error (RMSE)= 0.189. Finally, it can be concluded that the model can be used effectively in forecasting water level fluctuations, providing valuable insights for reservoir management.