DOI : https://doi.org/10.18517/ijaseit.14.1.19660

Modeling Flood Susceptible Areas Using Deep Learning Techniques with Random Subspace: A Case Study of the Mae Chan Basin in Thailand

Surachai Chantee (1), Theeraya Mayakul (2)
(1) IT Management, Faculty of Engineering, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand
(2) IT Management, Faculty of Engineering, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Nakhon Pathom, 73170, Thailand
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How to cite (IJASEIT) :
Chantee, Surachai, and Theeraya Mayakul. “Modeling Flood Susceptible Areas Using Deep Learning Techniques With Random Subspace: A Case Study of the Mae Chan Basin in Thailand”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 1, Feb. 2024, pp. 344-57, doi:10.18517/ijaseit.14.1.19660.
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Flooding is a recurring global issue that leads to substantial loss of life and property damage.  A crucial tool in managing and mitigating the impact of flooding is using flood hazard maps, which help identify high-risk areas and enable effective planning and management. This study presents a study on developing a predictive model to identify flood-prone areas in the Mae Chan Basin of Thailand using machine learning techniques, precisely the random sub-space ensemble method combined with a deep neural network (RS-DNN) and Nadam optimizer. The model was trained using 11 geographic information system (GIS) layers, including rainfall, elevation, slope, distance from the river, soil group, NDVI, road density, curvature, land use, flow accumulation, geology, and flood inventory data. Feature selection was carried out using the Gain Ratio method. The model was validated using accuracy, precision, ROC, and AUC metrics. Using the Wilcoxon signed-rank test, the effectiveness was compared to other machine learning algorithms, including random tree and support vector machines. The results showed that the RS-DNN model achieved a higher classification accuracy of 97% in both the training and testing datasets, compared to random tree (93%) and SVM (82%). The model's performance was also validated by its high AUC value of (0.99), compared to a random tree (0.93) and SVM (0.82) at a significance level of 0.05. In conclusion, the RS-DNN model is a highly accurate tool for identifying flood-prone areas, aiding in effective flood management and planning.

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