Browsing by Author "Subbarayan, S."

Now showing 1 - 14 of 14

Assessing Coastal Aquifer to Seawater Intrusion: Application of the GALDIT Method to the Cuddalore Aquifer, India
(Elsevier, 2018) Subbarayan, S.; Kulithalai Shiyam Sundar, K.S.S.; Sivaranjani, S.
Similar to many other parts of the world, water demand has been increasing in coastal areas due to industrial development, urbanization, population growth, agriculture, and tourism. As these demands are met from groundwater, the underground aquifers show rapidly increasing trends of depletion concomitant with seawater intrusion (SWI), which in turn is becoming a major environmental issue. In this chapter, vulnerability to seawater intrusion in the Cuddalore coastal aquifer is assessed by using the GALDIT method. This ranking- and weight-driven approach, based on various aquifer characteristics of the region, helped to determine the level of salt water intrusion in each distinct hydrological setting. Results of the study have shown that the area near the coast has been highly affected by SWI. Higher SWI in industrialized and urbanized areas is observed, suggestive of anthropogenically enhanced vulnerability. About 9.97% of the study area falls into the high vulnerability region, and the very low vulnerability region comprises 22.03% of the study. © 2019 Elsevier Inc. All rights reserved.
Assessing Coastal Aquifer to Seawater Intrusion: Application of the GALDIT Method to the Cuddalore Aquifer, India
(Elsevier, 2019) Subbarayan, S.; Kulithalai Shiyam Sundar, K.S.S.; Sivaranjani, S.
Similar to many other parts of the world, water demand has been increasing in coastal areas due to industrial development, urbanization, population growth, agriculture, and tourism. As these demands are met from groundwater, the underground aquifers show rapidly increasing trends of depletion concomitant with seawater intrusion (SWI), which in turn is becoming a major environmental issue. In this chapter, vulnerability to seawater intrusion in the Cuddalore coastal aquifer is assessed by using the GALDIT method. This ranking- and weight-driven approach, based on various aquifer characteristics of the region, helped to determine the level of salt water intrusion in each distinct hydrological setting. Results of the study have shown that the area near the coast has been highly affected by SWI. Higher SWI in industrialized and urbanized areas is observed, suggestive of anthropogenically enhanced vulnerability. About 9.97% of the study area falls into the high vulnerability region, and the very low vulnerability region comprises 22.03% of the study. © 2019 Elsevier Inc. All rights reserved.
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.
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.
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.
Assessment of potentially vulnerable zones using geospatial approach along the coast of Cuddalore district, East coast of India
(Taylor and Francis Ltd., 2022) Kulithalai Shiyam Sundar, K.S.S.; Subbarayan, S.; Deka, P.C.; Devanantham, A.
Coastal zones constantly undergo rapid changes in shape, morphology, and the environment due to natural as well as human development activities. Thus, assessing the vulnerability of the coast has become an important matter of concern. The study area is about 33 km of coastal zone from the Gaddilam to the Vellar River of Cuddalore districts in Tamil Nadu, India. This region was affected during the 2004 tsunami that took place in the Indian Ocean and also influenced by many cyclones in the Bay of Bengal. The methodology is about preparing various thematic layers such as shoreline change, elevation data, coastal slope, bathymetry, mean tidal range, maximum surge, beach width, geomorphology, and sea-level rise. Rank and weights are assigned to these parameters using the Index Overlay method in Geographic Information System environment. Vulnerability zones of different magnitudes such as very high, high, moderate, low, and very low were classified. From the study it is found about 15% of the coast is under very high vulnerability, 10.2% of the study lies under high vulnerability, 35.4% of the study lies under the moderately vulnerable region, 24% and 15.4% of the area lies under low and very low vulnerable region, respectively. © 2020 Indian Society for Hydraulics.
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.
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
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
Monitoring Spatial and Temporal Scales of Shoreline Changes in the Cuddalore Region, India
(Elsevier, 2018) Subbarayan, S.; Kulithalai Shiyam Sundar, K.S.S.; Vishnuprasath, S.R.
Coastal zones are constantly undergoing changes in shape and environment due to natural processes and anthropogenic interventions. The study of shoreline change has become a matter of great concern in recent years. The measurement of shorelines is a key factor in coastal zone construction. A shoreline change study was carried out for a 33-km stretch of the Cuddalore coast between Gadilam and the Vellar River. Satellite images (2000, 2005, 2010, and 2015) were taken as an input dataset in a GIS platform. Automatic shoreline delineation was attempted by a masking technique using ENVI software. In this study, the modification of normalized difference water index (MNDWI) method extracted the raster shoreline-based contrast value of coastal pixels and thresholding techniques for segmenting water and land regions. DSAS software and reference digitized shoreline boundary data were used for the analysis of shoreline changes. End point rate (EPR) and net shoreline movement determination showed the northern part of the Uppanar River mouth under erosion (region A to C and E) and sediment deposition at an accretion rate of 7.6. m/year from EPR and 114. m from NSM. The maximum shoreline erosion rate was -. 3.8. m/year from EPR and -. 57. m from NSM. From these attempts and results, a methodical approach for detection and monitoring of shoreline changes on spatial and temporal scales of interest have been suggested. © 2019 Elsevier Inc. All rights reserved.
Monitoring Spatial and Temporal Scales of Shoreline Changes in the Cuddalore Region, India
(Elsevier, 2019) Subbarayan, S.; Kulithalai Shiyam Sundar, K.S.S.; Vishnuprasath, S.R.
Coastal zones are constantly undergoing changes in shape and environment due to natural processes and anthropogenic interventions. The study of shoreline change has become a matter of great concern in recent years. The measurement of shorelines is a key factor in coastal zone construction. A shoreline change study was carried out for a 33-km stretch of the Cuddalore coast between Gadilam and the Vellar River. Satellite images (2000, 2005, 2010, and 2015) were taken as an input dataset in a GIS platform. Automatic shoreline delineation was attempted by a masking technique using ENVI software. In this study, the modification of normalized difference water index (MNDWI) method extracted the raster shoreline-based contrast value of coastal pixels and thresholding techniques for segmenting water and land regions. DSAS software and reference digitized shoreline boundary data were used for the analysis of shoreline changes. End point rate (EPR) and net shoreline movement determination showed the northern part of the Uppanar River mouth under erosion (region A to C and E) and sediment deposition at an accretion rate of 7.6m/year from EPR and 114m from NSM. The maximum shoreline erosion rate was −3.8m/year from EPR and −57m from NSM. From these attempts and results, a methodical approach for detection and monitoring of shoreline changes on spatial and temporal scales of interest have been suggested. © 2019 Elsevier Inc. All rights reserved.
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.
Utility of Landsat Data for Assessing Mangrove Degradation in Muthupet Lagoon, South India
(Elsevier, 2018) Subbarayan, S.; Jegankumar, R.; Selvaraj, A.; Jacinth Jennifer, J.; Kulithalai Shiyam Sundar, K.S.S.
Mangrove swamps and forests are an essential interface of the coastal zone that provide various ecological and economic services contributing to coastal protection and carbon credits. The ever-changing land use along the coastal tract, especially saltpans and agricultural activities in the mangrove habitats, contribute to the reduction of mangrove swamp sprawl and degrade the mangrove forest. Remote sensing techniques are routinely used to provide spatial-temporal information on mangrove ecosystem distribution, species identification, health status, and population. By adopting supervised classification techniques using the capabilities of indices such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI), we attempt to map the spatio-temporal variations of the Muthupet Lagoon regions. The increase of land use changes in the vicinity of the Muthupet Lagoon drastically decrease the freshwater flow and create significant impacts on the mangrove habitat. The study described herein documented degradation of 40.3. ha of dense mangroves from 2013 to 2016, 135.5. ha from 2008 to 2013, and 166. ha from 1999 to 2008 due to high salinity, coastal erosion, and the intrusion of saltpans, aquaculture farmlands, and other human activities. The area of sparse mangroves increased by 38.2. ha between 2013 and 2016, by 42.7. ha from 2008 to 2013, and by 191.3. ha from 1999 to 2008 due to prominent restoration activities. © 2019 Elsevier Inc. All rights reserved.
Utility of Landsat Data for Assessing Mangrove Degradation in Muthupet Lagoon, South India
(Elsevier, 2019) Subbarayan, S.; Jegankumar, R.; Selvaraj, A.; Jennifer, J.; Kulithalai Shiyam Sundar, K.S.S.
Mangrove swamps and forests are an essential interface of the coastal zone that provide various ecological and economic services contributing to coastal protection and carbon credits. The ever-changing land use along the coastal tract, especially saltpans and agricultural activities in the mangrove habitats, contribute to the reduction of mangrove swamp sprawl and degrade the mangrove forest. Remote sensing techniques are routinely used to provide spatial-temporal information on mangrove ecosystem distribution, species identification, health status, and population. By adopting supervised classification techniques using the capabilities of indices such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI), we attempt to map the spatio-temporal variations of the Muthupet Lagoon regions. The increase of land use changes in the vicinity of the Muthupet Lagoon drastically decrease the freshwater flow and create significant impacts on the mangrove habitat. The study described herein documented degradation of 40.3ha of dense mangroves from 2013 to 2016, 135.5ha from 2008 to 2013, and 166ha from 1999 to 2008 due to high salinity, coastal erosion, and the intrusion of saltpans, aquaculture farmlands, and other human activities. The area of sparse mangroves increased by 38.2ha between 2013 and 2016, by 42.7ha from 2008 to 2013, and by 191.3ha from 1999 to 2008 due to prominent restoration activities. © 2019 Elsevier Inc. All rights reserved.