مشخصات پژوهش

عنوان	Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA)
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Climate change, flood susceptibility, biogeography-based optimization, BAT algorithm, ANFIS
چکیده	This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70%; 201 floods), and validation (30%; 86 floods) datasets for modelling process and evaluation. The Step-wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS-BBO had the highest accuracy in comparison with the ANFIS and ANFIS-BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.
پژوهشگران	مهدی پناهی (نفر ششم به بعد)، هیمن شهابی (نفر ششم به بعد)، داوود پروین نژاد (نفر پنجم)، عطااله شیرزادی (نفر چهارم)، سمیه علیزاده (نفر سوم)، محمد کریمی (نفر دوم)، محمد احمدلو (نفر اول)