This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.

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The quality and completeness of rainfall data is a critical aspect in time series analysis and for prediction of future water-related disasters. An accurate estimation of missing data is essential for better rainfall prediction results. Multilayer perceptron (MLP) neural networks have been applied to solve stochastic problems in data science. This study suggests a novel approach for estimating missing rainfall data using MLP neural networks based on three configurations that are represented by the monsoon season (MS), non-monsoon season, and non-seasonal variation. For this purpose, a mathematical model was created to analyze and predict the time series of daily rainfall data in Seoul, South Korea. Missing rainfall data were reconstructed using the rainfall data of the other five stations after removing rainfall data from station number two in three time periods. The results of this study indicate that the new architecture of the MLP can accurately predict the missing rainfall data, particularly in the MS configuration when using only the rainfall data obtained during the MS. The performance of the proposed model was tested using the following evaluation criteria: root mean square error, mean absolute error, correlation coefficient, mean absolute deviation, mean absolute percentage error, and standard deviation. The confusion matrix showed values of 89, 83, and 92% for accuracy, recall, and precision, respectively. This indicates that the proposed model can effectively perform rainfall data reconstruction and predict missing rainfall data accurately when the length of the statistical period is limited to the MS with a high volume of rainfall.

No abstract

The quality and completeness of rainfall data is a critical aspect in time series analysis and for prediction of future water-related disasters. An accurate estimation of missing data is essential for better rainfall prediction results. Multilayer perceptron (MLP) neural networks have been applied to solve stochastic problems in data science. This study suggests a novel approach for estimating missing rainfall data using MLP neural networks based on three configurations that are represented by the monsoon season (MS), non-monsoon season, and non-seasonal variation. For this purpose, a mathematical model was created to analyze and predict the time series of daily rainfall data in Seoul, South Korea. Missing rainfall data were reconstructed using the rainfall data of the other five stations after removing rainfall data from station number two in three time periods. The results of this study indicate that the new architecture of the MLP can accurately predict the missing rainfall data, particularly in the MS configuration when using only the rainfall data obtained during the MS. The performance of the proposed model was tested using the following evaluation criteria: root mean square error, mean absolute error, correlation coefficient, mean absolute deviation, mean absolute percentage error, and standard deviation. The confusion matrix showed values of 89, 83, and 92% for accuracy, recall, and precision, respectively. This indicates that the proposed model can effectively perform rainfall data reconstruction and predict missing rainfall data accurately when the length of the statistical period is limited to the MS with a high volume of rainfall.