Faculty Publications
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Item Spatial Mapping of Flood Susceptibility Using Decision Tree-Based Machine Learning Models for the Vembanad Lake System in Kerala, India(American Society of Civil Engineers (ASCE), 2023) Kulithalai Shiyam Sundar, P.; Kundapura, S.Floods have claimed the lives of countless people and caused significant property damage in many countries, putting their livelihoods in the jeopardy. The Vembanad lake system (VLS) in Kerala, India, has faced adverse mishappening during 2018, 2019, and 2021 floods in the state due to torrential rainfall. The goal of this research is to construct effective decision tree-based machine learning models such as adaptive boosting (AdaBoost), random forest (RF), gradient boosting machines (GBMs), and extreme gradient boosting (XGBoost) for integrating data, processing, and generating flood susceptibility maps. There are 18 conditioning parameters considered, which include seven categories and 11 numerical data. These seven categorical data were converted to numerical data, bringing the total amount of input data to 61. The recursive feature elimination (RFE) was utilized as the feature selection technique, and a total of 22 layers were chosen to feed into the machine learning models to generate the flood susceptibility maps. The efficiencies of the models were evaluated using receiver operating characteristic (ROC)-area under the ROC curve (AUC), F1 score, accuracy, and kappa. According to the results, the performance of all four models demonstrated their practical application; however, XGBoost fared well in terms of the model's metrics. For the testing data set, the ROC-AUC values of XGBoost, GBM, and AdaBoost are 0.90, whereas it was 0.89 for RF. The accuracy varied significantly among the four models, with XGBoost scoring 0.92, followed by GBM (0.88), RF (0.87), and AdaBoost (0.87). As a result, this map may be utilized for early mitigation actions during future floods, as well as for land-use planners and emergency managers, assisting in the reduction of flood risk in regions prone to this hazard. © 2023 American Society of Civil Engineers.Item Groundwater Potential Mapping for Mangaluru in India, a Coastal Urban Environment using Convolutional Neural Networks(Springer, 2025) Kundapura, S.; Venkatesh, A.K.; Kandpal, U.Groundwater is vital for sustaining life, particularly in regions facing water scarcity. Effective management of groundwater resources requires accurate mapping of potential groundwater zones. This research incorporates Convolutional Neural Networks (CNN) to map precisely Groundwater Potential (GWP) zones in Mangaluru, a coastal taluk in Karnataka, India. Suitability of ten GWP conditioning factors: Elevation, Slope, Aspect, Rainfall, Geology, Geomorphology, Soil, Land Use and Land Cover (LULC), Drainage Density, and Topographic Wetness Index (TWI) is considered using Multicollinearity analysis. The CNN model performance was compared with Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models, and it outperformed by achieving an overall accuracy of 94.23% and an Area under the Receiver Operating Characteristic (AUC-ROC) of 94%. The resulting GWP map was classified into three zones: high (74.98%), moderate (17.13%), and low (7.88%) potential. Validation using groundwater level data from twenty-nine monitoring wells yielded an accuracy of 77%. The findings demonstrate the effectiveness of CNN for GWP mapping and provide valuable insights for sustainable groundwater resource management, policy and decision-making. © The Institution of Engineers (India) 2025.