Cited by Enhancing Flood Susceptibility Modeling: a Hybrid Deep Neural Network with Statistical Learning Algorithms for Predicting Flood Prone Areas

Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization DOI

Amala Mary Vincent,

Parthasarathy Kulithalai Shiyam Sundar,

P. Jidesh

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 148, P. 110846 - 110846

Published: Sept. 13, 2023

Language: Английский

Citations

Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques DOI

Jatan Debnath,

Dhrubojyoti Sahariah,

Nityaranjan Nath

et al.

Modeling Earth Systems and Environment, Journal Year: 2023, Volume and Issue: 10(2), P. 2393 - 2419

Published: Dec. 16, 2023

Abstract Climate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including Himalayan foothill region India. The Jia Bharali River basin, situated this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to environment local communities. Developing accurate reliable susceptibility models is crucial for effective prevention, management, adaptation strategies. In study, we aimed generate a comprehensive zone model catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, Frequency Ratio model, Fuzzy Logic (FL) Multi-criteria Decision Making based Analytical Hierarchy Process evaluate of basin. results revealed that approximately one-third basin area fell within moderate very high flood-prone zones. contrast, over 50% was classified as low demonstrated strong performance, ROC-AUC scores exceeding 70% MAE, MSE, RMSE below 30%. FL AHP were recommended application among areas similar physiographic characteristics due their exceptional performance training datasets. This study offers insights policymakers, regional administrative authorities, environmentalists, engineers working region. By providing robust research enhances prevention efforts thereby serving vital climate strategy regions. findings also implications disaster risk reduction sustainable development areas, contributing global towards achieving United Nations' Sustainable Development Goals.

Language: Английский

Citations

A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models DOI

Javeria Sarwar,

Saud Khan, Muhammad Azmat

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(23), P. 33495 - 33514

Published: April 29, 2024

Language: Английский

Citations

Advanced modeling for flash flood susceptibility mapping using remote sensing and GIS techniques: a case study in Northeast Algeria DOI

Ayman Mansour,

Dounia Mrad,

Yassine Djebbar

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(2)

Published: Jan. 1, 2024

Language: Английский

Citations

Enhancing Flood Susceptibility Modeling: a Hybrid Deep Neural Network with Statistical Learning Algorithms for Predicting Flood Prone Areas DOI

Motrza Ghobadi,

Masumeh Ahmadipari

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(8), P. 2687 - 2710

Published: March 18, 2024

Language: Английский

Citations