[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128214-en":3,"doc-seo-128214-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128214,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Integration of Hard and Soft Supervised Machine Learning for Flood Susceptibility Mapping","Flooding is a destructive natural phenomenon causing major casualties and property losses worldwide. Efficient flood susceptibility mapping (FSM) supports flood risk management by identifying areas with higher spatial expansion probability of flood events. This study assesses prediction performance of hard and soft supervised machine learning classification in FSM using three ANN models—MLP, FART, and SOM—under different activation functions, trained and validated on Ajichay river basin flood inventory data with TOC/AUC metrics. Results show the best performance for MLP-S and assess factor influence via OFAT and AFAT.","1 Integration of Hard and Soft Supervised Machine Learning for Flood Susceptibility  \n2 Mapping  \n3 Soghra Andaryania, Vahid Nourani a, Ali Torabi Haghighib, Saskia Keesstrac,d  \n4 a Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, 5 Iran  \nb  \n6 Water, Energy and Environmental Engineering Research Unit, University of Oulu, 90570 Oulu, Finland  \n7 c Team Soil, Water and Land Use, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708RC  \n8 Wageningen, the Netherlands  \nd  \n9 Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan 2308, 10 Australia.  \n11  \n12 Abstract  \n13 Flooding is a destructive natural phenomenon that causes many casualties and property losses in  \n14 different parts of the world every year. Efficient flood susceptibility mapping (FSM) can reduce  \n15 the risk of this hazard, and has become the main approach in flood risk management. In this study, 16 we evaluated the prediction ability of artificial neural network (ANN) algorithms for hard and soft  \n17 supervised machine learning classification in FSM by using three ANN algorithms (multi-layer  \n18 perceptron (MLP), fuzzy adaptive resonance theory (FART), self-organizing map (SOM)) with  \n19 different activation functions (sigmoidal (-S), linear (-L), commitment (-C), typicality (-T)) . We  \n20 used these models for predicting the spatial expansion probability of flood events in the Ajichay  \n21 river basin, northwest Iran. Inputs to the ANN were spatial data on 10 flood influencing factors  \n22 (elevation, slope, aspect, curvature, stream power index, topographic wetness index, lithology, 23 land use, rainfall, and distance to the river) . The FSMs obtained as model outputs were trained and  \n24 tested using flood inventory datasets earned based on previous records of flood damage in the  \n25 region for the Ajichay river basin. Model validation was carried out using total operating  \n26 characteristic (TOC) with an area under the curve (AUC) . The highest success rate was found for 27 MLP-S (92.1%) and the lowest for FART-T (75.8%). The projection rate in the validation ofFSMs  \n28 produced by MLP-S, MLP-L, FART-C, FART-T, SOM-C, and SOM-T was found to be 90.1%, 29 89.6%, 71.7%, 70.8%, 83.8%, and 81.1%, respectively. Sensitivity analysis using one factor-at-a- 30 time (OFAT) and all factors-at-a-time (AFAT) demonstrated that all influencing factors had a  \n31 positive impact on modeling to generate FSM, with altitude having the greatest impact and  \n32 curvature the least.  \n33 Keywords: Flood susceptibility map; Artificial neural network; Classification; Total operating  \n34 characteristic with area under curve; Ajichay river basin-Iran.  \n35  \n36 1. Introduction  \n37 River flooding is one of the most destructive natural hazards, affecting local populations and  \n38 structures, causing morphological changes by transporting sediment and soil (Mirzaee et al., 2018), 39 damaging agricultural land, and resulting in severe economic losses (Penning-Rowsell et al., 2005;  \n40 Balica et al., 2009; Talbot et al., 2018) . River floods and flash floods arising from severe rain  \n41 events or sudden snowmelt or dam collapse have been occurring more frequently in recent years, 42 due to climate change (Ardalan et al., 2009; Sharifi et al., 2012; Hosseini et al., 2020) .  \n43 The global flood risk has increased by more than 40% over the past two decades and may increase  \n44 further in future due to global climate change and urbanization, with the largest increases in the 45 U.S, Asia, and Europe (Raj and Singh, 2012; Alfieri et al., 2017; Vaghefi et al., 2019) . Flooding  \n46 affected approximately 109 million people worldwide between 1995 and 2015, causing USD 75  \n47 billion in damage annually (UNISDR and CRED, 2015) . Iran is one of the most vulnerable areas  \n48 to river flooding in Asia (Sharifi et al., 2012; Vaghefi et al., 2019). Recently (e.g., in 2017 and in 49 April","cbCaifJnG42jA8oo","https://ap.wps.com/l/cbCaifJnG42jA8oo","pdf",9203834,1,41,"English","en",105,"# Abstract\n# Keywords\n# 1. 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