Ai-Supported Investigation of Hydrological Sustainability of Malakand-3 Hydropower Plant

Abstract

The increasing demand for renewable energy and the effects of climate change necessitate accurate hydrological monitoring and forecasting for hydropower plants. This study focuses on the Malakand-3 hydropower plant in Khyber Pakhtunkhwa, Pakistan, and aims to investigate the hydrological sustainability of its input water source in the view of changing climate conditions. The study area encompasses the Swat River Catchment characterized by its complex topography as well as its potential for irrigation and hydroelectric power generation. The research addresses the limitations of previous studies by incorporating a broader range of data, including discharge, precipitation, and temperature trends for the past decade. A hydrological model was developed and calibrated to simulate the Swat River streamflow during the recent past. Furthermore, the study utilized ARIMA and neural network forecasting methods to forecast future streamflow trends. The ARIMA model, specifically ARIMA (4,1,1) (0,1,0) [365], was found to be the most suitable for forecasting. The results indicated an increase in flood frequency and rising water levels in Swat River, which indicates advantages for future hydropower production but challenges for the working of Malakand-3 hydropower plant due to forecasted floods. The study highlights the significance of accurate forecasting for optimizing power plant operations and flood management. The findings also demonstrate the potential for hydrologically sustainable development and for installation of additional hydroelectric power plants along the Swat River. This work intends to contribute to enhancing the understanding of water flow dynamics in the study area and provides valuable insights for decision-making in the sectors such as renewable energy, water resources management, and disaster management.