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Title
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Flood susceptibility assessment in Hengfeng area coupling adaptiveneuro-fuzzy inference system with genetic algorithm and differential evolution
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Type
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JournalPaper
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Keywords
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Climate change, Flood susceptibility, ANFIS, Genetic algorithm
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Abstract
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Floods are among Earth's most common natural hazards, and they cause major economic losses and seriously affect peoples' lives and health. This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS. An adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling. The model considers thirteen hydrologic, morphologic and lithological parameters for the flood susceptibility assessment, and Hengfeng County in China was chosen for the application of the model due to data availability and the 195 total flood events. The flood locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%). The Step-wise Weight Assessment Ratio Analysis (SWARA) approach was used to assess the relation between the floods and influencing parameters. Subsequently, two data mining techniques were combined with the ANFIS model, including the ANFIS-Genetic Algorithm and the ANFIS-Differential Evolution, to be used for flood spatial modelling and zonation. The flood susceptibility maps were produced, and their robustness was checked using the Receiver Operating Characteristic (ROC) curve. The results showed that the area under the curve (AUC) for all models wasN0.80.The highest AUC value was for the ANFIS-DE model (0.852), followed by ANFIS-GA (0.849). According to the
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Researchers
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Nerantzis Kazakis (Not In First Six Researchers), Ioannis Kougias (Not In First Six Researchers), Wei Chen (Not In First Six Researchers), A-xing Zhu (Not In First Six Researchers), Junzhi Liu (Fifth Researcher), Tianwu Ma (Fourth Researcher), Ataollah Shirzadi (Third Researcher), Mahdi Panahi (Second Researcher), Haoyuan Hong (First Researcher)
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