Flood damage cost prediction using random forest / Ainul Najwa Azahari and Norlina Mohd Sabri

• Main Authors: Azahari, Ainul Najwa, Mohd Sabri, Norlina
• Format: Article
• Language: English
• Published: Universiti Teknologi MARA Cawangan Pulau Pinang 2024
• Subjects: Electronic data processing. Computer-aided engineering
• Online Access: https://ir.uitm.edu.my/id/eprint/93325/1/93325.pdf; https://doi.org/10.24191/esteem.v20iMarch.614.g72; https://ir.uitm.edu.my/id/eprint/93325/; https://doi.org/10.24191/esteem.v20iMarch.614.g72
• Institution: Universiti Teknologi Mara

Floods are one of nature's deadliest catastrophes, causing permanent and catastrophic damage on the socioeconomic system, agriculture and human life. The problems arise when floods could cause a lot of economic damage such as damage to buildings, agriculture and others. Flood damage estimation is a subject of study that has not received much attention. The objective of this research is to explore the Random Forest algorithm in the flood damage cost prediction. The damages specified by the Malaysia’s Department of Irrigation (JPS) are structures such as culverts, MTB bridges, riverbank ruins, concrete main channels, farm roads, hydrological stations, agricultural and water drainage, JPS pump houses and tyres in Terengganu. Terengganu is one of the states in Malaysia which has to endure floods during the monsoon season by the end of the year. The methods employed in this research include data collection, data pre-processing, backend engine coding and user interface design. This project was implemented using the Python programming language. The data were collected from the annual flood report provided by the JPS Negeri Terengganu. The research used the rainfall and streamflow data from the year 2012 to 2022 as attributes to forecast the cost of the JPS structures damages in Terengganu. The prediction results showed that the best model achieved the accuracy of 91.47% with a Mean Squared Logarithmic Error (MSLE) of 0.48 and Coefficient of Determination (R2 ) of 0.92. In the performance evaluation, the model with 80:20 training and testing data ratio produced the best result in predicting the flood damage cost. The potential enhancements to this research involve extending the scope to encompass all Malaysian states, incorporating diverse flooded structures and adding more input variables for a more improved and more reliable flood prediction system.