Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

Aman Arora; Alireza Arabameri; Manish Pandey; Masood A Siddiqui; U K Shukla; Dieu Tien Bui; Varun Narayan Mishra; Anshuman Bhardwaj

doi:10.1016/j.scitotenv.2020.141565

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

Sci Total Environ. 2021 Jan 1:750:141565. doi: 10.1016/j.scitotenv.2020.141565. Epub 2020 Aug 13.

Authors

Aman Arora¹, Alireza Arabameri², Manish Pandey³, Masood A Siddiqui⁴, U K Shukla⁵, Dieu Tien Bui⁶, Varun Narayan Mishra⁷, Anshuman Bhardwaj⁸

Affiliations

¹ Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India. Electronic address: aman.jmi01@gmail.com.
² Department of Geomorphology, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 9821, Iran.
³ University Center for Research & Development (UCRD), Chandigarh University, Mohali 140413, Punjab, India; Department of Civil Engineering, Chandigarh University, Mohali 140413, Punjab, India. Electronic address: manish.e7925@cumail.in.
⁴ Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India.
⁵ Center for Advanced Study in Geology, Institute of Science, Banaras Hindu University, Varanasi 221005, India.
⁶ Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam.
⁷ Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, India.
⁸ School of Geosciences, University of Aberdeen, Meston Building, King's College, Aberdeen AB24 3UE, UK; Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden.

PMID: 32882492
DOI: 10.1016/j.scitotenv.2020.141565

Abstract

This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of predicting flood susceptible areas in a low altitudinal range, sub-tropical floodplain environmental setting, like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster. This makes the region one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outperformed other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found to be highest for ANFIS-GA during training (0.886) & validation (0.883). Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models. This will further help establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant.

Keywords: ANFIS; Differential evolution (DE); Flood susceptibility mapping; Genetic algorithm (GA); Metaheuristic optimization; Middle ganga plain; Particle swarm optimization (PSO).