Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Author: search.filters.author.Devanantham, D.

Search Results

Now showing 1 - 7 of 7
  • No Thumbnail Available
    Item
    Assessing the impact of damage and government response toward the cyclone Gaja in Tamil Nadu, India
    (Elsevier, 2021) Devanantham, D.; Subbarayan, S.; Jennifer, J.J.; Kulithalai Shiyam Sundar, K.S.S.; Singh, L.; Sankriti, R.
    The cyclone is one of the most frequently occurring natural disaster in all tropical countries that interrupts the socioeconomic development. In India, the rate of cyclone occurrence has increased by almost 30%. Tamil Nadu state, India, becomes one of the most cyclone-prone regions in the country. Severe cyclonic storm Gaja made landfall on 16th November 2018 in Nagapattinam district in Tamil Nadu. It had sustained wind speeds of 100-110 km/h with gusts of up to 120 km/h. The storm brought a significant amount of rainfall of about 140-170 mm. According to the reports, 45 people lost their lives, and 76, 290 people were evacuated from low-lying areas and sheltered in 300 relief centers. In this study, we have discussed strategies on the response after the event, preparedness, relief, recovery operations, rehabilitation, reconstruction, violent conflicts, economic sustainability, infrastructure development, livelihood, and the cause for severe damage and resilience. © 2021 Elsevier Inc. All rights reserved.
  • No Thumbnail Available
    Item
    Assessing the impact of 2018 tropical rainfall and the consecutive flood-related damages for the state of Kerala, India
    (Elsevier, 2021) Kulithalai Shiyam Sundar, K.S.S.; Deka, P.C.; Subbarayan, S.; Devanantham, D.; Jacinth Jennifer, J.
    Flood is the relatively high flow in the river, markedly than the usual resulting in the inundation of low land. Usually, river floods when the river can no longer contain its discharge from its catchments. Flood is the costliest as well as a common natural disaster in the world devastating both life and economy to a greater extent. The state of Kerala has faced an unprecedented rainfall followed by severe floods in August 2018 with a death toll for 504. Kerala is the southernmost narrow strip of the coastal territory that slopes down the Western Ghats to reach the Arabian Sea with 14 districts in the state. According to the Central Water Commission (CWC), the state received 2346.6 mm of rain from June to 19th of August, which is 42% more than the average rainfall. The state received a tremendous rainfall of 758.6 mm in the first 20 days of August which is 164% more than the actual rainfall. With the heavy rainfall all over the state, floods prevailed by the end of July. Once again a massive spell of rainfall happened at 8th and 9th of August which led to further flooding in Wayanad district. Due to the continuous rainfall from the first week of June to August, water levels were almost near the Full Reservoir Level. So, the water was released from several dams due to the heavy rainfall in the catchment. Another intense spell of rainfall took place by the 14th of August and continued till 19th of August resulting in the massive flood throughout the state affecting 13 of the 14 districts leading to the evacuation of about 3.4 million people to the 12, 300 relief camp across the state making the worst flood in the century. 2018 Kerala flood caused extensive damage to the crops, building, and infrastructure; its associated aftermath of the flood resulted in a huge loss to its economic, social, and natural environment, accompanied by the 331 landslides across 10 districts. After ravaging by the flood, the state has faced communicable diseases leptospirosis, chicken pox, hepatitis A, malaria, and dengue resulting in a death toll for 180. Thus, this paper is tried to understand the impact of the tropical rainfall followed by the devastating flood that occurred in the state of Kerala in August 2018 and to understand the impact on the socioeconomic disturbances, its resilience aftermath the flood. © 2021 Elsevier Inc. All rights reserved.
  • No Thumbnail Available
    Item
    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) Devanantham, D.; Subbarayan, S.; Singh, L.; Jennifer, J.J.; Saranya, T.; Kulithalai Shiyam Sundar, 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
  • No Thumbnail Available
    Item
    Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India
    (Elsevier B.V., 2023) Subbarayan, S.; Devanantham, D.; Reddy, N.M.; Kulithalai Shiyam Sundar, P.; Janardhanam, N.; Sathiyamurthi, S.; Vivek, V.
    Kerala experiences a high rate of annual rainfall and flooding resulting in a frequent natural disaster. The objective of this study is to develop flood susceptibility maps for the Idukki district making use of Remote Sensing (RS) data, Geographic Information System (GIS), and Machine Learning (ML). In this study, five different ML models (Adaboost, Gradient boosting, Extreme Gradient Boosting (XGB), CatBoost, Stochastic Gradient Boosting (SGB)) are evaluated to determine flood susceptibility in Idukki district Kerala. There were a total of sixteen hydrometeorological parameters taken into account. Area under the curve (AUC) was used to evaluate the accuracy of various techniques in terms of both prediction and success rates. The validation results proved the efficiency of the individual techniques. The highest AUC was obtained by the SGB and GBC (92%), followed by that of the Adaboost with AUC 87%, and the lowest AUC was obtained by CatBoost, with AUC of 79%. The absence of data overfitting in all models demonstrates the efficacy of boosting techniques. The boosting algorithms penalize models that overfit the training set, which helps to decrease overfitting. Researchers and local governments could benefit from the proposed boosting techniques in the flood susceptibility mapping and mitigation strategies. © 2023
  • No Thumbnail Available
    Item
    Coastal vulnerability assessment for the coast of Tamil Nadu, India—a geospatial approach
    (Springer Science and Business Media Deutschland GmbH, 2023) Devanantham, D.; Subbarayan, S.; Kulithalai Shiyam Sundar, P.
    A coastal region is a section of land that borders a significant body of water, often the sea or ocean. Despite their productivity, they are sensitive to even little alterations in the outside environment. This study aims to develop a spatial coastal vulnerability index (CVI) map for the Tamil Nadu coast of India, which has diverse coastal and marine environments that are ecologically fragile zones. Climate change is expected to increase the intensity and frequency of severe coastal hazards, such as rising sea levels, cyclones, storm surges, tsunamis, erosion, and accretion, severely impacting local environmental and socio-economic conditions. This research employed expert knowledge, weights, and scores from the analytical hierarchy process (AHP) to create vulnerability maps. The process includes the integration of various parameters such as geomorphology, Land use and land cover (LULC), significant wave height (SWH), rate of sea level rise (SLR), shoreline change (SLC), bathymetry, elevation, and coastal inundation. Based on the results, the very low, low, and moderate vulnerability regions comprise 17.26%, 30.77%, and 23.46%, respectively, whereas the high and very high vulnerability regions comprise 18.20% and 10.28%, respectively. The several locations tend to be high and very high due to land-use patterns and coastal structures, but very few are contributed by geomorphological features. The results are validated by conducting a field survey in a few locations along the coast. Thus, this study establishes a framework for decision-makers to implement climate change adaptation and mitigation actions in coastal zones. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
  • No Thumbnail Available
    Item
    Multi-Criterion Analysis of Cyclone Risk along the Coast of Tamil Nadu, India—A Geospatial Approach
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Subbarayan, S.; Devanantham, D.; Kulithalai Shiyam Sundar, P.; Reddy, N.M.; Almohamad, H.; Al-Dughairi, A.A.; Al-Mutiry, M.; Abdo, H.G.
    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. © 2023 by the authors.
  • No Thumbnail Available
    Item
    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) Devanantham, D.; Subbarayan, S.; Kulithalai Shiyam Sundar, K.S.S.; Reddy, N.M.; Niraimathi, J.; Bindajam, A.A.; Mallick, J.; AlHarbi, M.M.; Abdo, H.G.
    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.