Integration of hard and soft supervised machine learning for flood susceptibility mapping

Soghra Andaryani; Vahid Nourani; Ali Torabi Haghighi; Saskia Keesstra

doi:10.1016/j.jenvman.2021.112731

Integration of hard and soft supervised machine learning for flood susceptibility mapping

J Environ Manage. 2021 Aug 1:291:112731. doi: 10.1016/j.jenvman.2021.112731. Epub 2021 May 4.

Authors

Soghra Andaryani¹, Vahid Nourani², Ali Torabi Haghighi³, Saskia Keesstra⁴

Affiliations

¹ Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran. Electronic address: s.andaryani@gmail.com.
² Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran; Near East University, Faculty of Civil and Environmental Engineering, Near East Boulevard, 99138, Nicosia, North Cyprus, via Mersin 10, Turkey.
³ Water, Energy and Environmental Engineering Research Unit, University of Oulu, 90570, Oulu, Finland.
⁴ Team Soil, Water and Land Use, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708RC, Wageningen, the Netherlands; Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, 2308, Australia.

PMID: 33962279
DOI: 10.1016/j.jenvman.2021.112731

Abstract

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.

Keywords: Ajichay river basin-Iran; Artificial neural network; Classification; Flood susceptibility map; Total operating characteristic with area under curve.

MeSH terms

Floods*
Iran
Neural Networks, Computer
Rivers*
Supervised Machine Learning