Conference Papers

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    Overview of Water Resources in Kerala and Feasibility of Coastal Reservoirs to Ensure Water Security
    (Springer Science and Business Media Deutschland GmbH, 2022) Amala Krishnan, U.S.; Kolathayar, S.
    Kerala is rich with the beauty of nature, greenery, backwaters, rivers, etc. All the rivers are entirely monsoon-fed and many of them shrink into rivulets or dry up completely during dry months. The total runoff of all rivers adds to about 70,300 million cubic meters. The average rainfall of the State is reported as 3055 mm, which is double the national average. The water received as precipitation drains quickly into the sea due to the physiographical pattern and topography of the region. The farming activities get affected adversely due to the erratic rainfall pattern, which in turn affects the food security of the state. This paper presents the current scenario of water resources in the state and proposes alternative ways to ensure water security considering the unique geography of the state. The annual water demand of Kerala state is around 45.36 TMC feet and the total runoff of all rivers adds to about 2500 TMC feet. Kerala’s coast spans over 570 km and has excellent potential to store freshwater in coastal reservoirs. The capacity to store the water is huge without acquiring land and zero displacements of people. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Feature Elimination and Comparative Assessment of Machine Learning Algorithms for Flood Susceptibility Mapping in Kerala, India
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kundapura, S.; Aditya, B.; Apoorva, K.V.
    Floods are a catastrophic phenomenon with far-reaching consequences for infrastructure, the economy, and human lives, profoundly impacting regions globally. This study assesses flood susceptibility in four districts of Kerala: Ernakulam, Idukki, Kottayam, and Alappuzha. For the 2018 storm that caused flooding by Cyclone Ockhi, a flood map for the area was produced using Sentinel 1 satellite data in Google Earth Engine environment. The resulting map served as the foundation for further analysis. Based on the literature review, 16 potential flood causative factors were identified and incorporated into spatial maps in the Geographic Information System (GIS) environment. Analysis of the flood dataset was performed using Machine Learning (ML) algorithms, namely, Random Forest (RF), Decision Tree (DT), Gradient Boosting Machine (GBM), and XG Boost (XGB). Grid search was employed to identify the optimal hyperparameters for each algorithm, ensuring improved performance. Recursive Feature Elimination (RFE) was subsequently applied to select the most influential variables, resulting in a refined dataset. The chosen factors' feature importance scores were obtained, which were used to create the flood susceptibility map using the four ML models in a GIS environment. Evaluation metrics such as F1 score, accuracy, precision, recall, and ROC-AUC score were computed for each model, providing insights into the effectiveness of each algorithm in predicting the flood occurrence. The resulting flood susceptibility map for the best-performing ML model visually represents the varying levels of flood risk in the study area. © 2023 IEEE.
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    Mapping of 2018 Flood and Estimation of Future Flood Inundation Region for Vembanad Lake System in Kerala, India Using Sentinel-1 SAR Imagery
    (Springer Science and Business Media Deutschland GmbH, 2024) Kulithalai Shiyam Sundar, K.S.S.; Kundapura, S.
    Floods have claimed the lives of countless people and caused significant property damage, jeopardizing their livelihoods. The study area is the Vembanad Lake System in Kerala, India has faced severe flooding in 2018 due to torrential rainfall. Considering that Google Earth Engine (GEE) streamlines and simplifies the complex and time-consuming pre-processing of SAR images, this paper evaluates flood inundation mapping using Sentinel-1 SAR data for 2018. The flood inundation zone for the study is calculated using the Land Use Land Cover (LULC) map for 2018 and the forecasted LULC for 2035 and 2050. Hence, the research assesses the areas affected by floods in 2018 and those that may experience flooding of a similar degree in the near future. Thus, the extent of flood inundation during the 2018 floods and the potential flood inundation region for future LULC in 2035 and 2050 are determined. From the analysis, 14.7 km2 of built-up area was inundated during the 2018 floods. The 2018 flood event is used to quantify the flood that may inundate the future LULC in 2035 and 2050; it is found that the flood will affect about 19.87 km2 and 23.32 km2 of the built-up region, respectively. According to the study, the built-up area impacted by the flooding will increase by 34.99% and 58.4% from 2018 to 2035 and 2050, respectively. Examining the flood-prone areas and potential flood-affected areas in the future will be of great use to planners in their efforts to forewarn of an impending tragedy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.