Flood Prediction using Deep Spiking Neural Network
The aim of this article is to analyse the Deep Spiking Neural Network (DSNN) performance in flood prediction. The DSNN model has been trained and evaluated with 30 years of data obtained from the Drainage and Irrigation (DID) department of Sarawak from 1989 to 2019. The model's effectiveness is...
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my.unimas.ir.390422022-08-03T00:17:22Z http://ir.unimas.my/id/eprint/39042/ Flood Prediction using Deep Spiking Neural Network Roselind, Tei Abulrazak yahya, Saleh H Social Sciences (General) The aim of this article is to analyse the Deep Spiking Neural Network (DSNN) performance in flood prediction. The DSNN model has been trained and evaluated with 30 years of data obtained from the Drainage and Irrigation (DID) department of Sarawak from 1989 to 2019. The model's effectiveness is measured and examined based on accuracy (ACC), RMSE, Sensitivity (SEN), specificity (SPE), Positive Predictive Value (PPV), NPV and the Average Site Performance (ASP). Furthermore, the proposed model's performance was compared with other classifiers that are commonly used in flood prediction to evaluate the viability and capability of the proposed flood prediction method. The results indicate that a DSNN model of greater ACC (98.10%), RMSE (0.065%), SEN (93.50%), SPE (79.0%), PPV (88.10%), and ASP (89.60 %) is predictable. The findings were fair and efficient and outperformed the other BP, MLP, SARIMA, and SVM classification models NAUN 2022-07-26 Article PeerReviewed text en http://ir.unimas.my/id/eprint/39042/2/Flood%20Prediction%20-%20Copy.pdf Roselind, Tei and Abulrazak yahya, Saleh (2022) Flood Prediction using Deep Spiking Neural Network. International Journal of Circuits, Systems and Signal Processing,, 16. pp. 1045-1054. ISSN 1998-4464 https://npublications.com/journals/articles.php?id=453 DOI: 10.46300/9106.2022.16.127 |
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H Social Sciences (General) Roselind, Tei Abulrazak yahya, Saleh Flood Prediction using Deep Spiking Neural Network |
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The aim of this article is to analyse the Deep Spiking Neural Network (DSNN) performance in flood prediction. The DSNN model has been trained and evaluated with 30 years of data obtained from the Drainage and Irrigation (DID) department of Sarawak from 1989 to 2019. The model's effectiveness is measured and examined based on accuracy (ACC), RMSE, Sensitivity (SEN), specificity (SPE), Positive Predictive Value (PPV), NPV and the Average Site Performance
(ASP). Furthermore, the proposed model's performance
was compared with other classifiers that are commonly
used in flood prediction to evaluate the viability and
capability of the proposed flood prediction method. The
results indicate that a DSNN model of greater ACC
(98.10%), RMSE (0.065%), SEN (93.50%), SPE (79.0%),
PPV (88.10%), and ASP (89.60 %) is predictable. The
findings were fair and efficient and outperformed the
other BP, MLP, SARIMA, and SVM classification models |
format |
Article |
author |
Roselind, Tei Abulrazak yahya, Saleh |
author_facet |
Roselind, Tei Abulrazak yahya, Saleh |
author_sort |
Roselind, Tei |
title |
Flood Prediction using Deep Spiking Neural Network |
title_short |
Flood Prediction using Deep Spiking Neural Network |
title_full |
Flood Prediction using Deep Spiking Neural Network |
title_fullStr |
Flood Prediction using Deep Spiking Neural Network |
title_full_unstemmed |
Flood Prediction using Deep Spiking Neural Network |
title_sort |
flood prediction using deep spiking neural network |
publisher |
NAUN |
publishDate |
2022 |
url |
http://ir.unimas.my/id/eprint/39042/2/Flood%20Prediction%20-%20Copy.pdf http://ir.unimas.my/id/eprint/39042/ https://npublications.com/journals/articles.php?id=453 |
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