Browsing by Author "Kuruvilla, E."

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    Machine Learning Ensemble Model for Flood Susceptibility Mapping
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kundapura, S.; Soman, A.; Kuruvilla, E.
    Floods can be considered the most dangerous natural disaster, given their unpredictability and capacity to wipe out valuable life and property. The timely and efficient prediction of floods and flood susceptible or risk zones has been of utmost importance and can help with risk assessment, long-term management, and future preparedness. Over the years, Machine Learning (ML) has evolved as a powerful tool to build accurate flood models, inundation maps, and warning systems. This study uses the Support Vector Machine (SVM) and Random Forest (RF) algorithms and a Bagging Ensemble Model for Flood Susceptibility Mapping of Ernakulam, Kerala, India using multi-source geospatial data and the WEKA software. A total of twelve Flood Conditioning Factors (FCFs) are considered as variables, namely elevation, slope, curvature, Topographic Roughness Index (TRI), Topographic Wetness Index (TWI), Stream Power Index (SPI), rainfall, Land Use Land Cover (LULC), distance to the river, drainage density, and geology. The contribution of factors is assessed using the OneR Feature Selection method. The models are compared using the Receiver Operating Characteristics (ROC) curve and the Area Under the Curve (AUC) method. The Flood Susceptibility Map provides an opportunity for planners and authorities to flood preparation and long-term planning against flood impacts. © 2022 IEEE.
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    Performance Comparison of Machine Learning Algorithms in Groundwater Potability Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kuruvilla, E.; Kundapura, S.
    Rising global water demand has resulted in the overuse of groundwater resources and a decline in groundwater quality. Physical and chemical characteristics significantly impacted by geological formations and human activities determine how groundwater quality varies. An accurate and reliable assessment of groundwater resource information is the key element for effective management and enhancement of groundwater quality. The utilization of modern Machine Learning (ML) techniques in groundwater quality assessment provides insights for policymakers in suggesting remedies and management approaches for groundwater quality issues. Machine Learning models outperform other simulation models, using input and output datasets without considering the intricate relationship of the model to be analyzed and decreasing computational efforts. Comparison of various ML techniques, including Ensemble, Nonlinear, and Linear models for the prediction of groundwater potability is the main objective of this study. The presence of potable groundwater suggests that the water is fit for human consumption. The proposed approach makes use of eight ML algorithms i.e. Gradient Boosting Classifier (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Regression (LR) and Stochastic Gradient Descent (SGD) algorithm. According to the results, the Ensemble ML models outperformed well followed by the Nonlinear models, and Linear classification ML models have comparatively less accuracy and reliability. © 2022 IEEE.