Application of deep learning algorithm in hydrometry
by Mohammad Zakwan; Shaik A. Qadeer; Mohammed Yousuf Khan
International Journal of Hydrology Science and Technology (IJHST), Vol. 17, No. 1, 2024

Abstract: Estimation of discharge in a river is an integral part of water resource engineering. In this regard, various artificial intelligence (AI) techniques have been employed to model the discharge ratings. The present work compares the performance of two neural networks namely, back propagation neural network (BPNN) and radial basis neural network (RBNN), to model the discharge rating. The estimated discharge was also compared with the discharge estimated using conventional method. Published data of two gauging station was used for the comparative analysis. It was observed that application of neural networks greatly improved the estimates of discharge as compared to conventional method. Application of artificial neural network (ANN) reduced the sum of square of error (SSE) by about 90% on an average. Maximum absolute error was reduced from 51.36 and 141.21 to 5.04 and 7.68 respectively for the two stations for RBNN when compared to conventional method during validation. Calibration results reveal that among the BPNN and RBNN, RBNN could model the ratings at both the stations, better than BPNN.

Online publication date: Fri, 01-Dec-2023

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