Browsing by Author "Jennifer, J.J."

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    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.
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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) 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