Purushottam Kumar Mahato

Central University of Jharkhand

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Padam Jee Omar

Babasaheb Bhimrao Ambedkar University

Harinarayan Tiwari

Indian Institute of Technology Roorkee

Ravi Prakash Tripathi

Indian Institute of Technology BHU

Subash Prasad

Indian Institute of Technology Roorkee

Indira Khurana

Matthew Olomolatan Ibitoye

Federal University of Technology

Chinnasamy Pradipa

Mabel C. Marulanda

Research Center for Integrated Disaster Risk Management

Ning Niu

Henan University of Economic and Law

G. A. Dheebakaran

Tamil Nadu Agricultural University

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Tamil Nadu Agricultural University

Banaras Hindu University

Isfahan University of Technology

Indian Institute of Technology Roorkee

Indian Institute of Technology BHU

Indian Institute of Technology Guwahati

Ekiti State University

Obafemi Awolowo University

Universitat Politècnica de Catalunya

International Center for Numerical Methods in Engineering

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Exploring the Integration of Machine Learning for Environmental Pollution and Flood Risk Assessment

CRC Press eBooks (2024)

Padam Jee Omar Shashank Singh Purushottam Kumar Mahato Ghazaala Yasmin

Floods are natural disasters that pose significant risk to human lives, infrastructure, and the environment. In recent years, there has been growing interest in leveraging machine learning algorithms to improve flood prediction and management. This study explores the applications of machine learning in flood prediction and management, with a specific focus on addressing environmental pollution-related concerns. The development of predictive models involves analysing not only historical weather data and river levels but also considering factors such as pollutant levels and their impact on flood dynamics. The paper also discusses the potential benefits of these models in enhancing early warning systems, conducting comprehensive flood risk assessments that include environmental pollution aspects, and optimizing flood management strategies. By integrating machine learning techniques, decision-makers can make informed and timely decisions, leading to improved resilience and reduced impacts of both flooding and environmental pollution.

10.1201/9781032718323-8

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Assessing the impacts of human interventions and climate change on fluvial flooding using CMIP6 data and GIS-based hydrologic and hydraulic models

Geocarto International (2022)

Purushottam Kumar Mahato Dharmaveer Singh Birendra Bharati Alexandre S. Gagnon Bhupendra Bahadur Singh

This study presents an approach for modelling and mapping fluvial flooding, considering both land use/land cover (LULC) change and climate change, and applies it to the Brahmani River Basin in eastern India. Climate change projections were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and their impacts on the hydrology of the catchment were investigated using HEC-HMS and HEC-RAS software. Results reveal that changes in LULC types, specifically an increase in proportions of agricultural and built-up areas and a decrease in forest cover, as undergone between years 1985 and 2018, have increased peak discharge following a storm, thereby causing an increase in spatial extent of floods of different return periods. Moreover, downscaled climate change scenarios from two General Circulation Models were used to determine potential changes in river discharge according to two GHG emission scenarios from the latest IPCC: SSP245 and SSP585. The projections indicate that peak discharge and the spatial extent of flooded areas will increase for floods with return periods ranging from two to 100 years. This study demonstrates the important influence that changes in LULC have had on the susceptibility of the BRB to flooding, with climate change projected to further enhance the risk of flooding as the century progresses.

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Representative Concentration Pathways

10.1080/10106049.2022.2060311

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Elsevier eBooks (2024)

A. Adediji Priyadharshini Arul Gabriel A. Bernal Kulanthaisamy Bhuvaneshwari Omar D. Cardona

10.1016/b978-0-443-29802-8.00025-x

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Assessment of site suitability for surface water retention and springshed rejuvenation using geospatial techniques: a case study of Mizoram State