Journal Article

Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques

Hassan, Muhammad, Hashmi, Hashim Nisar, Ahmed, Imtiaz, Ghumman, Abdul Razzaq, Han, Dawei, Pasha, Ghufran Ahmed, Shamim, Muhammad Ali, Ashiq, Syed Zishan, Naeem, Usman Ali

2015

Overview

Population increase and climate change are stretching not only the world’s but also Pakistan’s water resources. This has directly been responsible for the recurring patterns of floods and droughts in the country which emphasizes the importance of the fact that efficient practices need to be adopted for water resource sustainability. This study investigates the use of upland catchment information, comprising of hydrometeorological datasets for inflow prediction to the Tarbela reservoir (a multipurpose reservoir located on River Indus) using Artificial Neural Networks (ANN) and Regression Techniques (Standard and Step Wise). Input Combination and data length selection for all the selected techniques were performed with the aid of Gamma test (GT). This study has made a significant contribution for future water resource management within the Indus Basin as Tarbela is the main source of irrigation, water supply and hydropower generation in Pakistan along with flood control.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Artificial neural networks / Climate change / Creeks & streams / Drought / Earth and Environmental Science / Earth Sciences / Earth System Sciences / Flood control / Geosphere / Hydroelectric power / Information Systems Applications (incl.Internet) / Irrigation water / Learning theory / Multipurpose reservoirs / Neural networks / Ontology / Pakistan / Regression / Remote sensing / Research Article / Reservoirs / Simulation and Modeling / Space Exploration and Astronautics / Space Sciences (including Extraterrestrial Physics / Stream discharge / Stream flow / Sustainability / Water inflow / Water resources / Water resources management / Water supply