Department of Civil Engineering

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Browsing Department of Civil Engineering by Author "Abijith, Devanantham"

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    Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India
    (Springer Science and Business Media Deutschland GmbH, 2025-01-27) Abijith, Devanantham; Saravanan, Subbarayan; Parthasarathy K.S.S.; Reddy, Nagireddy Masthan; Niraimathi, Janardhanam; Bindajam, Ahmed Ali; Mallick, Javed; Alharbi, Maged Muteb; Abdo, Hazem Ghassan
    Flooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. To identify flooded areas, the study utilised Google Earth Engine (GEE), Sentinel-1 data, and 12 geospatial datasets from multiple sources. A random forest algorithm was used for LULC change and flood susceptibility mapping. The LULC data are classified for the years 2000, 2010, and 2020, and from the classified data, the LULC for years 2030, 2040, and 2050 are projected for the study. Four future climate scenarios (SSP 126, 245, 370, and 585) were used for the average annual precipitation from the Coupled Model Intercomparison Project 6 (CMIP6). The results showed that the random forest model performed better in classifying LULC and identifying flood-prone areas. From the results, it has been depicted that the risk of flooding will increase across all scenarios over the period of 2000–2100, with some decadal fluctuations. A significant outcome indicates that the percentage of the area transitioning to moderate and very high flood risk consistently rises across all future projections. This study presents a viable method for flood susceptibility mapping based on different climate change scenarios and yields estimates of flood risk, which can provide valuable insights for managing flood risks. © The Author(s) 2025.
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    GIS-based multi-criteria analysis for identification of potential groundwater recharge zones - a case study from Ponnaniyaru watershed, Tamil Nadu, India
    (KeAi Communications Co., 2020-02-21) Abijith, Devanantham; Saravanan, Subbarayan; Singh, Leelambar; Jennifer, Jesudasan Jacinth; Saranya, Parthasarathy K.S.S.
    Groundwater is one of the most vital natural resources; spatially varying in quality and quantity. Increased urbanisation and population creates tremendous pressure on the quality and quantity of the groundwater resources. In this study, Ponnaniyaru watershed of Cauvery basin was considered for this research. Geographical information system (GIS) and remote sensing (RS) plays a vital role in preparing various thematic layers for targeting the groundwater potential zones (GWPZ). This study adopts the Analytical Hierarchy Process (AHP) and Multi influence factor (MIF), multi-criteria decision-making approaches to determine the weights for the influencing factors. Weighted linear overlay analysis was carried out to determine the GWPZ. Further, the resultant GWPZ map has been reclassified into five different classes, namely Very good, Good, Moderate, Poor and Very poor. The results were validated with observed well-yield data, and the predictive precision for AHP and MIF was found to be 75%, and 71% respectively. © 2020 The Authors
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    Multi-Criterion Analysis of Cyclone Risk along the Coast of Tamil Nadu, India—A Geospatial Approach
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-08-16) Saravanan, Subbarayan; Abijith, Devanantham; Kulithalai Shiyam Sundar, Parthasarathy; Reddy, Nagireddy Masthan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah; Al-Mutiry, Motrih; Abdo, Hazem Ghassan
    A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affected. With a few coastal cities with large population densities, Tamil Nadu’s coast is the third-most cyclone-prone state in India. This study involves the generation of a cyclone risk map by utilizing four distinct components: hazards, exposure, vulnerability, and mitigation. The study employed a Geographical Information System (GIS) and an Analytical Hierarchical Process (AHP) technique to compute an integrated risk index considering 16 spatial variables. The study was validated by the devastating cyclone GAJA in 2018. The resulting risk assessment shows the cyclone risk is higher in zones 1 and 2 in the study area and emphasizes the variations in mitigation impact on cyclone risk in zones 4 and 5. The risk maps demonstrate that low-lying areas near the coast, comprising about 3%, are perceived as having the adaptive capacity for disaster mitigation and are at heightened risk from cyclones regarding population and assets. The present study can offer valuable guidance for enhancing natural hazard preparedness and mitigation measures in the coastal region of Tamil Nadu.
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    Streamflow simulation using conceptual and neural network models in the Hemavathi sub-watershed, India
    (Elsevier B.V., 2022-11-21) Reddy, Nagireddy Masthan; Saravanan, Subbarayan; Abijith, Devanantham
    Water is one of the most valuable natural resources and a major element of a state's and country's socioeconomic growth. The world's water resources and India are under huge pressure because of rising demand and a limited supply. Proper water management is the only solution for ensuring a close gap between demand and supply. Hydrological modeling offers an answer to this issue by establishing relationships between different hydrological processes. Several models have been developed in the past decade to simulate the rainfall and runoff relations. Some models are simple conceptual models based on spatially distributed event-based or continuous and artificial intelligence (AI) models. This study aims to compare two conceptual daily-based models and one AI model developed for the Hemavathi sub-watershed in the Cauvery Basin (India). Two daily runoff models are implemented using conceptual models, i.e., Sacramento and the Australian water balance model (AWBM) using Rainfall-Runoff Library (RRL) tool and Feed forward Backpropagation neural network (FFBPNN) model. The models were calibrated for daily streamflow values from 1990 to 2006 and then validated from 2007 to 2015. The effectiveness of model runoff predictions is evaluated using statistical parameters such as Nash-Sutcliffe efficiency (NSE) and Correlation coefficient (CC) values. The NSE values for the FFBPNN is 0.88 (calibration) and 0.74 (validation), Sacramento model is 0.66 (calibration) and 0.48 (validation), and 0.63 (calibration) and 0.44 (validation) for the AWBM model. From the obtained results, the FFBPNN model performs well in terms of NSE and CC compared to Sacramento and AWBM models. © 2022