Faculty Publications
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Item 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.Item Spatiotemporal variation in the water quality of Vembanad Lake, Kerala, India: a remote sensing approach(Springer Science and Business Media Deutschland GmbH, 2023) Kulithalai Shiyam Sundar, K.S.S.; Kundapura, S.Water quality is one of the essential parameters of environmental monitoring; even a slight variation in its characteristics may significantly influence the ecosystem. The water quality of Vembanad Lake is affected by anthropogenic effects such as industrial effluents and tourism. The optical parameters representing water quality, such as diffuse attenuation (Kd), turbidity, suspended particulate matter (SPM), and chlorophyll-a (Chl-a), are considered in this study to evaluate the water quality of Vembanad Lake, Kerala, India. As this lake is regarded as of ecological importance by the Ramsar Convention and has faced severe concerns over recent years, there was a substantial change in the water quality during the lockdowns of the COVID-19 pandemic. This research is aimed at examining the change in water quality using optical data from Sentinel-2 satellites in the ACOLITE processing software from 2016 to 2021. The analyses showed a 2.5% decrease in the values of Kd, whereas SPM and turbidity show a reduction of about 4.3% from the year 2016 to 2021. The flood and the COVID lockdown had an impact on the improvement in the quality of water from 2018 to 2021. The findings indicated that the reduction in industrial activities and tourism had a more significant effect on the improvement in the water quality of the lake. There was no substantial change in the Chl-a until 2020, whereas an average decrease of 12% in Chl-a values was observed throughout 2021. This decrease can be attributed to the reduction in the lake’s hydrological residence time (HRT). Thus, these findings will be a valuable reference to help the government and non-government organizations (NGO) during strategic planning. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization(Elsevier Ltd, 2023) Vincent, A.M.; Kulithalai Shiyam Sundar, K.S.S.; Padikkal, J.Flooding is one of the most common natural hazards that have extremely detrimental consequences. Understanding which areas are vulnerable to flooding is crucial to addressing these effects. In this work, we use machine learning models and Automated machine learning (AutoML) systems for flood susceptibility mapping in Kerala, India. In particular, we used a three-dimensional convolutional neural network (CNN) architecture for this purpose. The CNN model was assisted with hyperparameter optimization techniques that combine Bayesian optimization with evolutionary algorithms like differential evolution and covariance matrix adaptation evolutionary strategies. The performances of all models are compared in terms of cross-entropy loss, accuracy, precision, recall, area under the curve (AUC) and kappa score. The CNN model shows better performance than the AutoML models. Evolutionary algorithm-assisted hyperparameter optimization methods improved the efficiency of the CNN model by 4 and 9 percent in terms of accuracy and by 0.0265 and 0.0497 with reference to the AUC score. © 2023 Elsevier B.V.