Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms
Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Blue Eyes Intelligence Engineering & Science Publication
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/80019/ http://irep.iium.edu.my/80019/1/80019_Rainfall-Rinoff%20Model%20Based%20on%20ANN.pdf |
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| author | Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar |
| author_facet | Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar |
| author_sort | Mohd Romlay, Muhammad Rabani |
| building | IIUM Repository |
| collection | Online Access |
| description | Rainfall-runoff model requires comprehensive
computation as its relation is a complex natural phenomenon.
Various inter-related processes are involved with factors such as
rainfall intensity, geomorphology, climatic and landscape are all
affecting runoff response. In general there is no single rainfallrunoff model that can cater to all flood prediction system with
varying topological area. Hence, there is a vital need to have
custom-tailored prediction model with specific range of data, type
of perimeter and antecedent hour of prediction to meet the
necessity of the locality. In an attempt to model a reliable
rainfall-runoff system for a flood-prone area in Malaysia, 3
different approach of Artificial Neural Networks (ANN) are
modelled based on the data acquired from Sungai Pahang,
Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficeint of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional
ANN learning algorithm. Our case study takes the data of
rainfall and runoff from the year 2012 to 2014. This is a case
study in Pahang river basin, Pekan, Malaysia. |
| first_indexed | 2025-11-14T17:48:00Z |
| format | Article |
| id | iium-80019 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T17:48:00Z |
| publishDate | 2019 |
| publisher | Blue Eyes Intelligence Engineering & Science Publication |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-800192020-04-20T05:52:48Z http://irep.iium.edu.my/80019/ Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar TK Electrical engineering. Electronics Nuclear engineering Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfallrunoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficeint of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia. Blue Eyes Intelligence Engineering & Science Publication 2019-09-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/80019/1/80019_Rainfall-Rinoff%20Model%20Based%20on%20ANN.pdf Mohd Romlay, Muhammad Rabani and Rashid, Muhammad Mahbubur and Toha @ Tohara, Siti Fauziah and Mohd Ibrahim, Azhar (2019) Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms. International Journal of Recent Technology and Engineering, 8 (3). pp. 971-979. ISSN 2277-3878 https://www.ijrte.org/wp-content/uploads/papers/v8i3/C4115098319.pdf C4115098319/19©BEIESP |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms |
| title | Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms |
| title_full | Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms |
| title_fullStr | Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms |
| title_full_unstemmed | Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms |
| title_short | Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms |
| title_sort | rainfall-rinoff model based on ann with lm, br and pso as learning algorithms |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://irep.iium.edu.my/80019/ http://irep.iium.edu.my/80019/ http://irep.iium.edu.my/80019/ http://irep.iium.edu.my/80019/1/80019_Rainfall-Rinoff%20Model%20Based%20on%20ANN.pdf |