Forecasting the discharge of the Zayandeh Rood River at the Ghleeh Shahrokh station using deep learning techniques
Subject Areas : watere sciences
1 - Department of Computer Engineering, shoushtar Branch, Islamic Azad University, shoushtar, Iran.
Keywords: ANFIS, Neural network, Prediction, river discharge, LSTM,
Abstract :
Abstract- Water discharge is a term in the water industry that refers to the amount of water that passes through a certain point per unit of time. Discharge rate is the amount of water that passes through a specific point such as a river,, water channel, dam valve, pipe or any other structure such as a faucet cartridge in a unit of time. In the metric system, water discharge rate is expressed in terms of cubic meters per second, cubic meters per hour, or liters per second. The unit of cubic meters per second is used for large flows such as rivers and large canals, and the unit of liters per second is used for the flow of water in wells and water that enters leaks. Measuring the discharge of the river has many effects on people's lives. Knowing the amount of water entering the areas of a river's catchment area is very important in agriculture, potential risks to human and animal life, industries, etc. Therefore, predicting river discharge can lead to effective management and prevent serious damage in the mentioned areas. According to the mentioned cases, the purpose of the presented paper is to predict the river discharge using deep learning techniques. In order to do this, the discharge of the Zayandeh Rood River at Qala Shahrokh station has been investigated and predicted using two techniques - ANFIS and LSTM. The simulation results show 93% to 94% accuracy in predicting the discharge of the studied river.
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