Semi-distributed neural network models for streamflow prediction in a small catchment pinang
This paper applied an artificial intelligence methodology for streamflow prediction in a flash flood in Pinang catchment based on TOPMODEL input and output data sets. TOPMODEL is a semi-distributed rainfall runoff model widely used in numerous water resource applications. However, literature has ind...
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Gheorghe Asachi Technical University of Iasi, Romania
2019
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author | Suliman, Ali H. Ahmed Mat Darus, Intan Zaurah |
author_facet | Suliman, Ali H. Ahmed Mat Darus, Intan Zaurah |
author_sort | Suliman, Ali H. Ahmed |
collection | ePrints |
description | This paper applied an artificial intelligence methodology for streamflow prediction in a flash flood in Pinang catchment based on TOPMODEL input and output data sets. TOPMODEL is a semi-distributed rainfall runoff model widely used in numerous water resource applications. However, literature has indicated relative weakness in TOPMODEL performances in streamflow prediction. Thus, radial basis function neural network (RBF-NN) has been employed to improve the accuracy of streamflow prediction and then compared with TOPMODEL and multilayer perceptron neural network (MLP-NN) performances. Four years of daily hydro-meteorological data sets (for the period between 2007 to 2010) were used for calibration and validation analysis. The results have shown an improvement from 0.749 and-19.2 of the calibration period to 0.957 and 0.001, and from 0.774 and-19.84 of the validation period to 0.956 and-3.611 of Nash-Sutcliffe model (NS) and Relative Volume Error (RVE), respectively. RBF-NN performance has been established to improve the daily streamflow prediction; however, the MLP-NN was better in contrast with the involved method in the study. It can be concluded that TOPMODEL performance showed a high ability to simulate the peaks compared with both AI methodologies. |
first_indexed | 2024-03-05T20:46:36Z |
format | Article |
id | utm.eprints-89072 |
institution | Universiti Teknologi Malaysia - ePrints |
last_indexed | 2024-03-05T20:46:36Z |
publishDate | 2019 |
publisher | Gheorghe Asachi Technical University of Iasi, Romania |
record_format | dspace |
spelling | utm.eprints-890722021-01-26T08:44:15Z http://eprints.utm.my/89072/ Semi-distributed neural network models for streamflow prediction in a small catchment pinang Suliman, Ali H. Ahmed Mat Darus, Intan Zaurah TA Engineering (General). Civil engineering (General) This paper applied an artificial intelligence methodology for streamflow prediction in a flash flood in Pinang catchment based on TOPMODEL input and output data sets. TOPMODEL is a semi-distributed rainfall runoff model widely used in numerous water resource applications. However, literature has indicated relative weakness in TOPMODEL performances in streamflow prediction. Thus, radial basis function neural network (RBF-NN) has been employed to improve the accuracy of streamflow prediction and then compared with TOPMODEL and multilayer perceptron neural network (MLP-NN) performances. Four years of daily hydro-meteorological data sets (for the period between 2007 to 2010) were used for calibration and validation analysis. The results have shown an improvement from 0.749 and-19.2 of the calibration period to 0.957 and 0.001, and from 0.774 and-19.84 of the validation period to 0.956 and-3.611 of Nash-Sutcliffe model (NS) and Relative Volume Error (RVE), respectively. RBF-NN performance has been established to improve the daily streamflow prediction; however, the MLP-NN was better in contrast with the involved method in the study. It can be concluded that TOPMODEL performance showed a high ability to simulate the peaks compared with both AI methodologies. Gheorghe Asachi Technical University of Iasi, Romania 2019-02 Article PeerReviewed Suliman, Ali H. Ahmed and Mat Darus, Intan Zaurah (2019) Semi-distributed neural network models for streamflow prediction in a small catchment pinang. Environmental Engineering and Management Journal, 18 (2). pp. 535-544. ISSN 1582-9596 http://dx.doi.org/10.30638/eemj.2019.050 DOI:10.30638/eemj.2019.050 |
spellingShingle | TA Engineering (General). Civil engineering (General) Suliman, Ali H. Ahmed Mat Darus, Intan Zaurah Semi-distributed neural network models for streamflow prediction in a small catchment pinang |
title | Semi-distributed neural network models for streamflow prediction in a small catchment pinang |
title_full | Semi-distributed neural network models for streamflow prediction in a small catchment pinang |
title_fullStr | Semi-distributed neural network models for streamflow prediction in a small catchment pinang |
title_full_unstemmed | Semi-distributed neural network models for streamflow prediction in a small catchment pinang |
title_short | Semi-distributed neural network models for streamflow prediction in a small catchment pinang |
title_sort | semi distributed neural network models for streamflow prediction in a small catchment pinang |
topic | TA Engineering (General). Civil engineering (General) |
work_keys_str_mv | AT sulimanalihahmed semidistributedneuralnetworkmodelsforstreamflowpredictioninasmallcatchmentpinang AT matdarusintanzaurah semidistributedneuralnetworkmodelsforstreamflowpredictioninasmallcatchmentpinang |