Comprehensive evaluation of machine learning algorithms for flood susceptibility mapping in Wardha River sub-basin, India

Aug 3, 2026

Machine learning enables robust flood susceptibility mapping by exploiting complex environmental data to improve predictive performance. This study evaluates seven prominent algorithms for flood susceptibility mapping in the Wardha River sub-basin, India, at multiple spatial resolutions (30 m to 200 m). Seven flood-inducing factors are analyzed and model quality is judged using sensitivity, specificity, AUC, correlation, overall SD ratio, and RMSD. SVM, GBM, and RF show higher sensitivity to spatial resolution, while XGB ranks best overall; LDA is notable for execution time efficiency and resilience.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

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Abstract

Evaluated algorithms and data factors

Model assessment metrics and spatial resolution effects

Results ranking and key findings

Introduction

Climate change and flood risk context

Flood susceptibility mapping and mitigation role

Methods overview: MCDM, hydrological models, statistical approaches, soft computing

Acta Geophysica [https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1600-024-01471-8 Comprehensive evaluation of machine learning algorithms for flood susceptibility mapping inWardha River sub‑basin, India Asheesh Sharma1,2 · Sudhanshu Nerkar3 · Rishit Banyal3 · Mandeep Poonia3 · Rakesh Kadaverugu3 · Lalit Damahe4 · Franziska Tügel5,6 · Ekkehard Holzbecher7 · Reinhard Hinkelmann8 Received: 3 July 2024 / Accepted: 7 October 2024 © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2024 Abstract Machine learning offers a powerful and versatile approach to flood susceptibility mapping, enabling us to leverage complex data and improve prediction accuracy. Given the plethora of available techniques and the challenges in selecting the optimal approach, this study investigates prominent ML algorithms for flood susceptibility mapping (FSM) in the Wardha River sub-basin, India. Seven machine learning algorithms, viz. support vector machine (SVM), extreme gradient boosting (XGB), artificial neural network (ANN), generalized linear model (GLM), gradient boosting machine (GBM), random forest (RF), and linear discriminant analysis (LDA), were evaluated at varying spatial resolutions (30 m, 50 m, 100 m, and 200 m) . Seven flood-inducing factors (elevation, flow accumulation, topographic wetness index, slope, rainfall, land use, and drain density) were considered. Model performance was assessed using sensitivity, specificity, area under the curve (AUC), overall correlation, overall standard deviation ratio, and overall root mean square difference (RMSD) . The impact of spatial resolution on models’ accuracy was analysed. SVM, GBM, and RF were significantly affected, while ANN, GLM, and XGB were less sensitive. LDA excelled in execution time and spatial resolution resilience. The overall ranking of models was executed based on their accuracy, AUC, and execution time. XGB outperformed GBM and RF, securing first place, while SVM ranked last. GLM, ANN, and LDA ranked third to fifth. The results highlighted the importance of algorithm selection in accurately mapping flood susceptibility, particularly when working with varying spatial resolution data. The study findings can inform the decision-making process for implementing FSM using these machine learning algorithms. Keywords Flood susceptibility mapping · Machine learning algorithms · Flood-contributing factor · Spatial resolution · Execution time Introduction The accelerating pace of climate change and rising global temperatures have significantly disrupted global weather patterns. These disruptions are visible in the form of unpredictable rainfall and drought conditions worldwide. Abrupt, torrential downpours are causing widespread flooding around the world. Large-scale human activities have increased the vulnerability of landscapes to flooding. India is a frequent victim of devastating floods among Asian nations. India's floods are primarily driven by persistent monsoon rainfall, coupled with factors such as reduced river capacity due to Edited by Dr. Bahram Choubin (ASSOCIATE EDITOR) / Prof. Jochen Aberle (CO-EDITOR-IN-CHIEF) . Extended author information available on the last page of the article erosion and siltation, inadequate natural drainage in floodprone regions, and occasional cloudbursts. Moreover, rapid urbanization and altered land use patterns have exacerbated flood risks, particularly in urban areas, creating a new challenge in recent decades (Mohanty et al. 2020) . While floods remain the most frequent natural disaster, their impacts can be mitigated through early warning systems. Flood susceptibility mapping (FSM) studies, which produce flood hazard maps, play a crucial role in this effort. By identifying floodprone areas, flood mapping enables communities to reduce the negative impacts of flooding through proactive planning and preparedness (Mishra and Prasad 2024) . Methods for identifying flood-prone

FAQs

Which machine learning algorithms were evaluated for flood susceptibility mapping?

Seven algorithms were tested: SVM, XGB, ANN, GLM, GBM, RF, and LDA.

What flood-inducing factors and spatial resolutions were used?

Seven factors were considered: elevation, flow accumulation, topographic wetness index, slope, rainfall, land use, and drain density. Models were evaluated at 30 m, 50 m, 100 m, and 200 m.

How was model performance assessed in the study?

Performance was measured using sensitivity, specificity, AUC, overall correlation, overall standard deviation ratio, and overall root mean square difference (RMSD). Execution time was also analyzed, especially for resilience across resolutions.

Aug 3, 2026

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