Soghra Andaryani, Vahid Nourani, Ali Torabi Haghighi, Saskia Keesstra, Integration of hard and soft supervised machine learning for flood susceptibility mapping, Journal of Environmental Management, Volume 291, 2021, 112731, ISSN 0301-4797, https://doi.org/10.1016/j.jenvman.2021.112731
Integration of hard and soft supervised machine learning for flood susceptibility mapping
|Author:||Andaryani, Soghra1; Nourani, Vahid1,2; Torabi Haghighi, Ali3;|
1Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
2Near East University, Faculty of Civil and Environmental Engineering, Near East Boulevard, 99138, Nicosia, North Cyprus, via Mersin 10, Turkey
3Water, Energy and Environmental Engineering Research Unit, University of Oulu, 90570, Oulu, Finland
4Team Soil, Water and Land Use, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708RC, Wageningen, the Netherlands
5Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, 2308, Australia
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021070941280
|Publish Date:|| 2023-05-04
Flooding is a destructive natural phenomenon that causes many casualties and property losses in different parts of the world every year. Efficient flood susceptibility mapping (FSM) can reduce the risk of this hazard, and has become the main approach in flood risk management. In this study, we evaluated the prediction ability of artificial neural network (ANN) algorithms for hard and soft supervised machine learning classification in FSM by using three ANN algorithms (multi-layer perceptron (MLP), fuzzy adaptive resonance theory (FART), self-organizing map (SOM)) with different activation functions (sigmoidal (-S), linear (-L), commitment (-C), typicality (-T)). We used integration of these models for predicting the spatial expansion probability of flood events in the Ajichay river basin, northwest Iran. Inputs to the ANN were spatial data on 10 flood influencing factors (elevation, slope, aspect, curvature, stream power index, topographic wetness index, lithology, land use, rainfall, and distance to the river). The FSMs obtained as model outputs were trained and tested using flood inventory datasets earned based on previous records of flood damage in the region for the Ajichay river basin. Sensitivity analysis using one factor-at-a-time (OFAT) and all factors-at-a-time (AFAT) demonstrated that all influencing factors had a positive impact on modeling to generate FSM, with altitude having the greatest impact and curvature the least. Model validation was carried out using total operating characteristic (TOC) with an area under the curve (AUC). The highest success rate was found for MLP-S (92.1%) and the lowest for FART-T (75.8%). The projection rate in the validation of FSMs produced by MLP-S, MLP-L, FART-C, FART-T, SOM-C, and SOM-T was found to be 90.1%, 89.6%, 71.7%, 70.8%, 83.8%, and 81.1%, respectively. While integration of hard and soft supervised machine learning classification with activation functions of MLP-S and MLP-L showed a strong flood prediction capability for proper planning and management of flood hazards, MLP-S is a promising method for predicting the spatial expansion probability of flood events.
Journal of environmental management
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
218 Environmental engineering
1172 Environmental sciences
Soghra Andaryani was supported by the Iran's National Elites Foundation (INEF) (grant agreement: 15/7806).
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.