Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Temporal Assessment of Meteorological Drought Events Using Stationary and Nonstationary Drought Indices for Two Climate Regions in India(American Society of Civil Engineers (ASCE), 2023) Sajeev, A.; Kundapura, S.This study attempts to build nonstationary indices for assessing meteorological drought in two different climate zones in India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop nonstationary models using the generalized additive model in location, scale, and shape (GAMLSS) for the period, 1951-2004. A comparative study has been conducted to assess the statistical performance of stationary and nonstationary models on various time scales (3, 6, 12, and 24 months). The best model is selected to conduct copula-based bivariate drought analysis. For this purpose, drought properties such as drought severity, duration, and peak are calculated. The annual and seasonal rainfall departures are also analyzed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The nonstationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The nonstationary drought index shows better consistency with historical drought and flood events than the stationary index. Cooccurrence and joint return periods are calculated and compared with univariate return periods. A significant difference is observed between bivariate and univariate return periods, and more risk is detected in Saurashtra and Kutch than in Coastal Karnataka. The impacts of rainfall and drought on the yield of major crops in study areas are also analyzed. The yield loss rate of bajra significantly correlates with the nonstationary standardized precipitation index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. This new aspect of drought analysis provides feasible results in both arid and humid regions in a changing environment. © 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.