Ahmad Jazli, Abdul Rahman and Nor Azuana, Ramli (2024) Flood prediction using artificial neural networks: A case study in Temerloh, Pahang. Qeios (TUZ29Y). pp. 1-13. ISSN 2632-3834. (Published)
|
Pdf
TUZ29Y.pdf Available under License Creative Commons Attribution. Download (490kB) | Preview |
Abstract
Flood is one of the natural disasters that causes damage to properties, and sometimes loss of lives. Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. Temerloh is one of the districts in Pahang that frequently encounters flood events, especially between November and January every year. Even though there are multiple efforts in flood mitigation and preparation, the damage to citizens and properties every year has cost thousands of Ringgits and the time taken to clean the damages caused by floods. Despite this, research on flood prediction in the state needs to be done using machine learning techniques. Due to this, this research explored the hydrological and meteorological factors that caused the flood in Temerloh and developed a machine-learning model capable of predicting the flood occurrence. The study used a dataset from the National Hydrological Network Management System (SPRHiN), which consists of hydrological data, and weather underground for the meteorological data in the location. The correlation analysis found that stream flow and water level are highly correlated to floods, with correlation coefficients (r values) of 0.83 and 0.76, respectively, while the temperature is inversely related to floods with a -0.28 correlation value. A lower temperature has a higher chance of rain and subsequent flooding. The results show that the model, by using an artificial neural network (ANN), has produced an accuracy of 0.9909 and a good performance of the area under the receiver operating characteristic curve (ROC) curve (AUC) at 0.888. The model also shows a low error with the mean squared error (MSE) of 0.009 and the root-mean-squared error (RMSE) of 0.096. The R2 value of 0.768 and F1 value of 0.875 indicate that the model has high precision and recall. Afterwards, a flood monitoring dashboard was created to visualize the data interactively. This research is vital in understanding the flood factors in Pahang and would offer academic insight for future research in floods. In addition, the flood monitoring dashboard will significantly assist governments and authorities in focusing the flood management efforts in areas at high risk of flood and be used to aid the state's future development.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Machine Learning; Artificial Neural Network; Flood in Pahang; Flood Monitoring Dashboard |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Center for Mathematical Science Institute of Postgraduate Studies |
| Depositing User: | Dr. Nor Azuana Ramli |
| Date Deposited: | 24 Apr 2024 04:30 |
| Last Modified: | 24 Apr 2024 04:30 |
| URI: | http://umpir.ump.edu.my/id/eprint/41053 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |