GIS-based rainfall-runoff neuro model for streamflow prediction
TOPMODEL is a semi-distributed rainfall runoff model that has been widely used in numerous water resources’ applications in the last few decades. However, literature has identified the weakness in the TOPMODEL performances in streamflow prediction. In this paper, a multilayer perceptron neural netwo...
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| Format: | Article |
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Praise Worthy Prize S.r.l
2017
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| _version_ | 1796862494138957824 |
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| author | Suliman, A. H. A. Mat Darus, I. Z. Katimon, A. |
| author_facet | Suliman, A. H. A. Mat Darus, I. Z. Katimon, A. |
| author_sort | Suliman, A. H. A. |
| collection | ePrints |
| description | TOPMODEL is a semi-distributed rainfall runoff model that has been widely used in numerous water resources’ applications in the last few decades. However, literature has identified the weakness in the TOPMODEL performances in streamflow prediction. In this paper, a multilayer perceptron neural network (MLP-NN) has been adopted to improve the accuracy of streamflow prediction in a flash flood in Pinang catchment area. Two daily hydro-meteorological datasets of year 2007-2008 and 2009-2010 were used for calibration and validation periods, respectively. The new method presented in this study uses the TOPMODEL input-output datasets during the calibration period to train the MLP-NN to predict the output. Then, the trained MLPNN model structure is used to predict the streamflow based on validation period datasets. The three efficiencies considered to evaluate the model performances are the Nash-Sutcliffe model (NS), the Relative Volume Error (RVE) and the Correlation Coefficient (CoC). The results indicated an improvement from 0.749,-19.2 and 0.893 of NS, RVE and CoC of the calibration period to 0.978, 0.364 and 0.989, respectively. Moreover, for the validation periods, the performance has been improved from 0.774,-19.84 and 0.933 of NS, RVE and CoC to 0.975,-0.029 and 0.789, respectively. The ability of MLP-NN to improve TOPMODEL daily streamflow prediction has been demonstrated in this study. |
| first_indexed | 2024-03-05T20:12:28Z |
| format | Article |
| id | utm.eprints-76372 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:12:28Z |
| publishDate | 2017 |
| publisher | Praise Worthy Prize S.r.l |
| record_format | dspace |
| spelling | utm.eprints-763722018-06-29T22:25:06Z http://eprints.utm.my/76372/ GIS-based rainfall-runoff neuro model for streamflow prediction Suliman, A. H. A. Mat Darus, I. Z. Katimon, A. TJ Mechanical engineering and machinery TOPMODEL is a semi-distributed rainfall runoff model that has been widely used in numerous water resources’ applications in the last few decades. However, literature has identified the weakness in the TOPMODEL performances in streamflow prediction. In this paper, a multilayer perceptron neural network (MLP-NN) has been adopted to improve the accuracy of streamflow prediction in a flash flood in Pinang catchment area. Two daily hydro-meteorological datasets of year 2007-2008 and 2009-2010 were used for calibration and validation periods, respectively. The new method presented in this study uses the TOPMODEL input-output datasets during the calibration period to train the MLP-NN to predict the output. Then, the trained MLPNN model structure is used to predict the streamflow based on validation period datasets. The three efficiencies considered to evaluate the model performances are the Nash-Sutcliffe model (NS), the Relative Volume Error (RVE) and the Correlation Coefficient (CoC). The results indicated an improvement from 0.749,-19.2 and 0.893 of NS, RVE and CoC of the calibration period to 0.978, 0.364 and 0.989, respectively. Moreover, for the validation periods, the performance has been improved from 0.774,-19.84 and 0.933 of NS, RVE and CoC to 0.975,-0.029 and 0.789, respectively. The ability of MLP-NN to improve TOPMODEL daily streamflow prediction has been demonstrated in this study. Praise Worthy Prize S.r.l 2017 Article PeerReviewed Suliman, A. H. A. and Mat Darus, I. Z. and Katimon, A. (2017) GIS-based rainfall-runoff neuro model for streamflow prediction. International Review of Civil Engineering, 8 (5). pp. 235-240. ISSN 2036-9913 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036609982&doi=10.15866%2firece.v8i5.12104&partnerID=40&md5=11a820a7d4787c34d2b564713179e6eb |
| spellingShingle | TJ Mechanical engineering and machinery Suliman, A. H. A. Mat Darus, I. Z. Katimon, A. GIS-based rainfall-runoff neuro model for streamflow prediction |
| title | GIS-based rainfall-runoff neuro model for streamflow prediction |
| title_full | GIS-based rainfall-runoff neuro model for streamflow prediction |
| title_fullStr | GIS-based rainfall-runoff neuro model for streamflow prediction |
| title_full_unstemmed | GIS-based rainfall-runoff neuro model for streamflow prediction |
| title_short | GIS-based rainfall-runoff neuro model for streamflow prediction |
| title_sort | gis based rainfall runoff neuro model for streamflow prediction |
| topic | TJ Mechanical engineering and machinery |
| work_keys_str_mv | AT sulimanaha gisbasedrainfallrunoffneuromodelforstreamflowprediction AT matdarusiz gisbasedrainfallrunoffneuromodelforstreamflowprediction AT katimona gisbasedrainfallrunoffneuromodelforstreamflowprediction |