Modeling of Flow Rate at Sukkur Barrage using Artificial Neural Networks (ANNs)

Abstract

Modeling of flow discharge plays a significant role in effective planning, sustainable usage, development, and management of water resources in short (hourly) and long-term (monthly) temporal categories. Since the inception of managing water resources, various techniques such as conceptual, metric, and physical models have been introduced all of these require a large amount of data, labor, and expense to be incorporated to obtain reliable results, due to which Artificial Intelligence methods were introduced that require less amount of data, time, expense and as well as experience to model flow discharge. In this research study, an attempt was made by employing two different artificial neural network techniques feedforward neural networks (FFNN), and time-lagged neural networks (TLNN) to model and predict the river flow discharge at daily and monthly timescale. 2010 and 503 no. of observations were used for model calibration and validation in daily time scale while 557 and 139 observations were used in monthly timescale. The result of the study revealed that the FFNN modeling approach has captured the daily and monthly stream flow variability very well than the TLNN model with R2 of 0.91 on the daily and 0.71 on the monthly time scale while R2 for the TLNN model was 0.79, and 0.34 for daily and monthly timescale. This indicates that the FFNN technique requires less no. of observations and is more reliable than TLNN and can be used to model river flow.