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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
NAUN
2022
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| Subjects: | |
| Online Access: | 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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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | 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 |
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