Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

Manish Pandey,Yogesh Ray,Alireza Arabameri,Aman Arora,Uk Shukla,Jagriti Mishra,Varun Narayan Mishra,Hoang Nguyen,Masood Ahsan Siddiqui,Naveen Kumar,Sangeeta Soni,Romulus Costache

doi:10.3389/feart.2021.659296

Abstract

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

Highlights

Floods in the changing climatic and anthropogenic scenario over the Holocene period have been impacting the living conditions of humans (Macklin and Lewin, 2003)
This study reveals that support vector machine (SVM)- and fuzzy-WofEbased ensemble has the capability to perform much better, accuracy-wise, in like Middle Ganga Plain (MGP) topoclimatic setting, with freely available moderate quality DEM-like ASTER 30 m
To achieve the goal of flood susceptible area zonation of MGP based on flood susceptibility prediction index (FSPI) produced by applying different ensembles of models, this study is the in the series of models’ testing after Arora et al (2021b)

Summary

Introduction

Floods in the changing climatic and anthropogenic scenario over the Holocene period have been impacting the living conditions of humans (Macklin and Lewin, 2003). Hydrological, climatic, and societal factors, floods have been variously classified (Sikorska et al, 2015). The widely accepted definition of flood encompasses the views of hydrologists, hazard managers, and sociologists, i.e., floods occur when the rise of water levels, caused by meteorological, hydrological, geomorphic, anthropological, and societal factors, can result in inundated areas which otherwise remain dry thereby causing loss of life, agriculture (including livestock), and property (Hubbart and Jones, 2009). Apart from tectonically induced ground subsidence, landuse/landcover (LULC) induced (Kumar et al, 2018), climate change-induced (Arora et al, 2021a), river embankment breach induced (Bhatt et al, 2010), etc., factors cause frequent flooding in the GRB. Many aspects of floods are quantifiable using continuously growing remote sensing satellite technology and their output products (Plaza et al, 2009)

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Discussion

Conclusion