1. Ph.D Theses
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Item Dynamic Analysis of Offshore Floating Wind Turbine Combined with Wave Energy Converter(National Institute of Technology Karnataka, Surathkal, 2024) J. S, RONY; KARMAKAR, DEBABRATAThe combined offshore wind and wave energy on an integrated platform is an economical solution for the offshore energy industry as they share the infrastructure and ocean space. The study presents the dynamic analysis of the Submerged Tension-Leg Platform (STLP) and Frustum Tension-Leg Platform (FTLP) combined with a heaving-type point absorber wave energy converter (WEC). The feasibility study of the hybrid concept is performed using the aero-servo-hydro-elastic simulation. The study analyses the responses of the combined system to understand the influence of the WECs on the STLP and FTLP platforms for various operating conditions of the wind turbine under regular and irregular waves. The platform responses are analysed for the North Atlantic wave region. A positive synergy is observed between the platform and the WECs, and the study focuses on the forces and moments developed at the interface of the tower and platform to understand the effect of wind energy on the turbine tower and the importance of motion amplitudes on the performance of the combined platform system. Further, the hydrodynamic performance of circular and concentric arrangements of cone-cylinder-type heaving WECs around STLP and FTLP is analysed. The influence of the hydrodynamic coefficients is analysed by determining the ratio of the hydrodynamic coefficients for a single WEC system to those for a hybrid system. The study analyses the instantaneous wave power absorbed and the wave power under the influence of PTO for the WECs arranged around the TLP floaters. The rigid body analysis observed reduced motion response for the STLP+6WECs and FTLP+8WECs configurations. The dynamic responses of the hybrid platforms for different mooring layouts are studied for different met ocean conditions. The time history and spectrum of the generator power are analysed to observe the effect of second-order wave load and turbulent wind loads on the power production of the hybrid floater under different mooring configurations. Further, the most probable values of the motion amplitudes are calculated using long-term response analysis for the hybrid wave and wind energy system. The long-term distribution is performed using the short-term responses based on Rayleigh distribution and North Atlantic wave data. The transfer function for the long-term analysis of the floater is obtained using the numerical simulation tool FAST. The analysis is performed for zero-degree wave heading angle and different operational conditions of the wind turbine. Thereafter, the reliability of hybrid floating wind turbine platforms against extreme loads is established using the Inverse First Order Reliability Method (IFORM) which includes the randomness in the gross wind environment and the extreme response given wind conditions. The maximum values of the responses for both 1-D and 2-D models are studied and compared. The probability of the exceedance of the responses (Surge, sway, and yaw) for the platforms is studied for different return periods. The study suggests the best possible arrangement pattern for wave power absorption and power uniformity among the floaters in the array. The study performed will be helpful in the design and analysis of the combined wave and wind energy device for wave power absorption.Item Hydrodynamic Performance of Submerged Tandem Breakwaters and Integrated Hybrid Floating Structures(National Institute of Technology Karnataka, Surathkal, 2024) PATIL, SHIVAKUMAR B.; KARMAKAR, DEBABRATAIn the present study, the gravity wave interaction with submerged tandem breakwater of different structural configurations and integrated hybrid Floating structures are investigated based on the small-amplitude wave theory. The boundary value problem is analysed in two- dimension using Multi-Domain Boundary Element Method (MDBEM) considering the linearized wave theory in finite water depth. The wave transformation characteristics, wave forces and wave energy dissipation are analysed with and without the presence as reef structure in front of the primary submerged breakwater. The comparative study is performed for the submerged tandem breakwaters of various shapes (trapezoidal, triangular, rectangular, and thin-walled) and types (rubble mound, permeable, impermeable) that are designed to function together as a tandem breakwater. The effect of varying angle of incidence, relative submergence depth, and relative gap between the reef structure and primary breakwater on wave induced force, wave reflection, transmission and energy dissipation characteristics are derived for the suggested submerged breakwater and tandem breakwater models. In case of floating structures, the hydrodynamic characteristics of Fixed Floating Structure (FFS) of various configurations such as Rectangular Fixed Floating Structures (RFFS) and Trapezoidal Fixed Floating Structures (TFFS) coupled with submerged breakwaters of two different shapes namely, rectangular breakwater (RBW) and trapezoidal breakwater (TBW) are investigated for the variation in physical parameters such as a change in structural parameters of the submerged breakwater (shape, relative submergence depth, relative crest width, and structural porosity), structural parameters of FFS (shape and structural width), wave parameter (angle of incidence) and relative spacing between the FFS and submerged breakwater. Furthermore, three typical Submerged Floating Tunnel (SFT) cross-sections (rectangular, trapezoidal, and circular) of equal area and structural height in the presence of submerged rubble mound breakwater (SRMB) under similar operating conditions are investigated as comparative study to investigate the influence of SFT shape on hydrodynamic performance. Further, in the case of Wave Energy Converter (WEC) integrated into a Pile-Restrained rectangular Floating Breakwater (PRFB) in presence of a partially reflecting vertical seawall is analysed for scattering and radiation problems under a framework of small amplitude linear wave theory using the Boundary Element Method (BEM). The linear power take-off (PTO) damping is employed to calculate the absorbed power, while the rectangular floating breakwater is designed to heave with pile restrained in position. In addition, on assuming only iv a small amount of sloshing within the tank of the hybrid floating breakwater (within the application scope of linear potential wave theory), the WEC capabilities and the hydrodynamic coefficients (wave reflection and transmission coefficients) were estimated for a certain range of excitation frequencies with the help of numerical and experimental investigations. The study on the submerged tandem breakwater structure and floating structures illustrates that the presence of the reef like structure decreases aids in lowering the wave stresses inflicted on the primary breakwaters and aids in optimum wave transformations. On the other hand, studies on WEC integrated with structures can assist the designer to determine the appropriate degrees of efficiency of the WEC device without sacrificing the hydrodynamic performance by fine- tuning the system's geometrical parameters.Item Flood Modeling and Mapping in the Upper Awash River Basin, Ethiopia(National Institute of Technology Karnataka, Surathkal, 2024) TOLA, SINTAYEHU YADETE; SHETTY, AMBAClimate variability, land cover change, and catchment characteristics significantly impact hydrological extremes. However, their impact on flood response behavior varies spatiotemporally, and quantifying possible causes is essential for effectively mitigating floods. Identifying the potential flood areas, and flood hazard mapping, considering changing environmental factors and an alternative flood frequency model for developing flood hazard management and mitigation strategies are crucial in the flood-prone basin. This thesis comprehensively investigates the potential factors affecting and explaining floods, possible flood sites, and flood hazard maps over the Upper Awash River Basin (UARB), Ethiopia, during the study period from 1985 to 2015. First, the study investigated the variability of extreme hydroclimatic conditions and the relationship between anomalies in extreme local precipitation, El Niño Southern Oscillation indicators (ENSO) (Southern Oscillation Index (SOI), Niño 3.4, and Multivariate ENSO Index (MEI)), and extreme flow indices. The analysis used standardized anomaly index and coefficient of variation statistics to examine variability, the modified Mann-Kendall and Pettitt tests for trend and change point analysis, and Spearman's correlation test to explore relationships. Results showed that the basin-wise extreme precipitation indices had less variability but higher variability spatially, while the extreme flow indices showed high variability. The maximum temperature increased significantly, while the minimum temperature decreased significantly (except at a few northwest stations), with a considerable shift in the 1990s and 2000s. Anomalies and a decrease in extreme precipitation were consistent with the extreme flow at the basin outlet, Hombole station. However, the extreme flow indices at Melka Kunture increased significantly and shifted upward (2003/2005), and the anomalies in extremely wet and very wet precipitation in the northwest were possibly responsible for this change. The annual wet and very wet days of precipitation strongly affected the extreme flow in the basin. The effect of annual wet day precipitation, maximum yearly precipitation, and ENSO anomalies on extreme flow at the Hombole were significant. ii Secondly, the study quantitatively assessed the effects of individual and coupled changes in land cover and climate on peak and high flows at Melka Kunture and Hombole over the baseline (1988-2001) and evaluation (2002-2015) period. The impact of these changes was estimated using the Soil Water and Assessment Tool (SWAT). The model satisfactorily simulated daily and extreme flows. The SWAT model showed that the main factor which affected the changes in upstream flow was the land cover change, increasing peak and high flow by 38.69% and 11.95%, respectively, compared to the baseline period. However, combined changes resulted in downstream peak and high flow reductions of 19.55% and 50.33%, respectively. In addition, the spatial flood characteristics based on morphometric parameters were performed in four subbasins to understand the hydrological behaviour better. The topographic wetness index (TWI) and topographic position index (TPI) were also used to determine the potential flood areas and inundation extent. The aggregated parameters revealed that subbasin SB-1 comprises Melka Kunture, is highly susceptible to flooding, SB-3 and SB-4 are moderately susceptible, and SB- 2 is low. The degree of susceptibility was also determined by incorporating the TWI and TPI through overlay analysis. The UARB accounts for 22.8%, 41.7%, and 35.6% of the total basin classified as high, medium, and low flood-prone, respectively. Furtherly the study developed a flood hazard map based on the nonstationary flood frequency using a generalized extreme value distribution model for the highly susceptible subbasin (SB-1), the identified flood spot area, Becho floodplain. The distributional location parameter was modeled as a function of rainfall amount of different durations, annual total precipitation from wet days, yearly mean maximum temperature, and time as covariates. The one-dimensional Hydrological Engineering Center River Analysis System (HEC-RAS) hydraulic model with steady flow analysis was used to generate flood hazard map input, depth and velocity, and inundation extent for different return periods. The result indicated that the model as a function of rainfall, such as monthly rainfall (August) and annual wet day precipitation, best fit the observed hydrological data. The developed hazard map based on depth alone and the combination of depth and velocity thresholds iii resulted in more than 70% of the floodplain area being classified as a high hazard zone under 2, 25, 50, and 100 years return periods.Item Integrated Surface Water Resource Modeling and Irrigation Productivity in Lower Baro, Ethiopia(National Institute of Technology Karnataka, Surathkal, 2024) DENEKE, FISEHA BEFIKADU; Shetty, Amba; Fufa, Ing. FekaduThe management of surface water resources is hampered in many river basins by a lack of data. The problem is for several of Ethiopia’s river basins increasing the productivity of surface irrigation and scientifically understanding the factors that led to integrated surface water modeling, particularly in Ethiopia's lower Baro is useful. The objectives of this study were (i) to review the land cover (LC) change implications to hydrological variables soil erodibility and yield reduction (ii) to explore statistical and trend analysis of hydrometeorological data, (iii) to quantify the surface water potential and irrigation water demand, and (iv) to investigate the satellite-ET based irrigation performance using Water Productivity Open-access Portal database and to come up with a strategy for quantifying the spatial and temporal increase water use efficiency (WUE) and system water use efficiency (sWUE) in the rainfed and irrigated area of lower Baro watershed. GIS-based multi-criteria evaluation used with the interaction of 8 factoring parameters, to see the low level of irrigation development. In the eight sub-classes, a total of 20, 325 km2 of appropriate pastoral land has been transformed into rainfed rice, sugarcane, maize, and vegetable land. This study used GIS, RS, Water Evaluation and Planning (WEAP), Cropwat8.0, and EasyFit software. The soil moisture rainfall-runoff method was computed using the WEAP hydrological model for the surface water demand and potential simultaneously from 2000-2014 and 2020-2030. This work used systematic reviews and a meta-analysis technique to examine the LC change and its effects on hydrological variables, soil erodibility, and yield reduction. Record identified through Scopus Searching, Web of Science Searches, and Google Scholar. Fully articles were assessed for eligibility and excluded for reasons. In the data search, 85 articles with investigations published between 2007 to 2022 were examined. Then, for surface water potential and irrigation productivity, only 2% of the abstracts that were eventually evaluated for assessment were selected. Four crops were selected to grow in these identified irrigable areas, and their gross irrigation demand was calculated using Cropwat8.0. In addition to annual streamflow variability, using graph pad prism 9.4 the monthly streamflow variability was determined. With the help of the WEAP system's parameter estimation tool weekly, average streamflow calibration was performed. II As a result, of the review, in the lower Baro, built-up/ settlement, agricultural land, water body, bare/ outcrop, and commercial farm rose roughly +195, +48, +35, +35, and +1%, respectively. On the other hand, shrubland, rangeland, forest land, and wetland decreased by 1, 0.5, 5, and 10%, respectively. But the revised universal soil loss equation looks to be a good alternative and soil water conservation measures are crucial for minimizing soil erodibility in lower Baro. As a result, of the water balance obtained the maximum estimated monthly hydropower potential, irrigation water demand, livestock water demand, and surface water potential were 60.3 Mm3 and the corresponding outflow value was 52.4 Mm3. The total estimated mean annual surface runoff leaving each station of the watershed was 462.06 Mm3. The coefficient of determination (R2) result was 0.88 and the Nash efficiency coefficient (NSE) was 0.91 highest at the Baro Gambella station. The anticipated irrigation requirement for the selected crop's driest five months of May, February, March, January, and April was 1, 0.9, 0.78, 0.78, and 0.34 l/s/h. The Baro Gambella sub-catchment had maximum critical values test results of σ = 12.6, μ =11.9, and γ = 0, while the Sor Metu showed the smallest value of 0.80, 1.75, and -0.03. Across the watershed, the sWUE varies with runoff, with a coefficient of variation of 71%. As a result, the overall accuracy of the LC change was 81%, the Landsat 8 images of the soiladjusted vegetation index showed a maximum value of 0.87 and a minimum of -1.5. The normalized vegetation index of 0.58 maximum and -1 minimum was observed. By 2050, the sWUE will be 10% lower temporally, but its spatial variability will be 25% higher. From 2017-2023 an increase in trees (dense forest), scrub/shrub land, flooded vegetation, and bare ground, while there had been a decline in water bodies and crops during the same period. When yield gaps are increased by a factor of 1/3, 2/3, and 3/4, the Baro Gambella sub-catchment has the biggest yield gaps 443.52, 887.04, and 1008.106 kg respectively, while the Gumero Gore sub-catchment experiences the smallest yield gaps 0.01, 0.02, and 0.03 kg respectively. As a result, the crop water productivity, ET, crop index, and temporal fluctuation of the yield gap were investigated. Also, the available dry river flow does not meet the available potential irrigable land. Moreover, to increase the irrigation crop water productivity by 2050 in the lower Baro watershed. Therefore, improve soil infiltration and water storage, which decreases runoff and the water lost by ET and raises sWUE.Item Response Evaluation of Environmental Flow Indicators to Land use Land Cover and Climate Change Over Three Humid Tropical River Basins(National Institute of Technology Karnataka, Surathkal, 2024) ABRAHAM, ALKA; KUNDAPURA, SUBRAHMANYAClimate change and Land Use Land Cover (LULC) change are two major factors influencing river basin hydrology. The present research explored the isolated and combined impacts of these drivers on the ecologically relevant flow in three humid tropical basins, Meenachil, Manimala, and Achencoil, located in Kerala, India. Climate change and associated extreme events have a critical influence on maintaining the socioeconomic stability of society. The study examined the trend in climate variables, precipitation, and temperature over historical and future time steps. The distribution of historic precipitation is assessed with Precipitation Concentration Indices (PCI). The trend analysis of the climatic variables is evaluated using Mann Kendall test and Sen’s slope at annual and seasonal time steps. The historical precipitation showed the predominance of an insignificant declining trend in annual, winter, pre-monsoon, and monsoon time steps. The post-monsoon rainfall showed a positive trend in the area. The maximum and minimum temperatures showed a prominent rising trend in annual and seasonal time steps. The nature of extreme events is evaluated with extreme climate indices. The trend exhibited by ten precipitation indices: Maximum daily rainfall (Rx1day), Maximum 5-day rainfall (Rx5day), Number of heavy rainfall days (R10), Number of very heavy rainfall days (R20), Consecutive Wet Days (CWD), Consecutive Dry Days (CDD), Annual wet day rainfall total (PRCPTOT), Simple Daily Intensity Index (SDII), Precipitation from very wet days (R95p), Extremely wet days (R99p) and 4 temperature indices: Warmest day (TXx), Coldest day (TXn), Warmest night (TNx), Coldest night (TNn) over the area are determined. There is a general trend in the area of a considerable decrease in the number of CWD and a significant increase in the absolute extremes, Rx1day and Rx5day. The results revealed the possibility of rainfall getting concentrated in fewer days. The extreme temperature indices showed rising trend with significant rise for TXx and TXn, which can be a signal of the climate warming in the region. The future climate changes in the study basins are analysed with the statistically downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs, NEX-GDDP, and Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset. The Multi-Criteria Decision Making (MCDM) approaches Compromise Programming ii (CP), and PROMETHE-2 are considered for selecting a suitable subset of GCMs in the research. The ensemble mean of the top four models for the considered scenarios is subjected to bias correction using the Delta Change and Distribution Mapping techniques. The bias-corrected future projections under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios and Shared Socioeconomic Pathways (SSP) 126, 245, 370, and 585 scenarios are further considered for the trend test for the period 2020-2099. The analyses reveal a decreasing trend in average annual precipitation under the RCP 4.5 scenario, while an increasing trend is observed under the RCP 8.5 scenario. The SSP scenarios showed a significant rising trend in annual rainfall for end-century time slices. However, the projected maximum and minimum temperatures showed a significant rise under all the scenarios. The extreme precipitation indices are noticed with a rising trend under the considered emission scenarios, which points out the possibility of more extreme rainfall events in these basins. The rising temperature extremes and frequent rainfall extremes emphasize the need for suitable adaptation strategies and mitigation measures in these basins. The LULC is crucial in influencing the processes that shape the Earth's surface. It is essential to comprehend the spatio-temporal dynamics of the LULC within a certain location. Thus, the LULC maps for the basins are studied with a Land Change Modeler (LCM) and utilised to predict future LULC. The Random Forest (RF) classifier in Google Earth Engine (GEE) was utilised for LULC classification for the years 1990, 2000, 2008, 2018 and 2021. The overall accuracy obtained for the years 1990, 2000, 2008, 2018, and 2021 were 0.92, 0.91, 0.97, 0.88, and 0.95, respectively, followed by a Kappa coefficient of 0.91, 0.89, 0.96, 0.85, and 0.94. LCM was explored for LULC change detection, the model was validated successfully in predicting the LULC distribution in 2021, and the results were comparable with the actual 2021 LULC. Results of the analysis revealed the changes undergone by various LULC classes in both basins. Historical LULC analysis showed the expansion of the Built-up area and Barren land in these basins. The study then utilised LCM to predict future LULC up to the year 2050 at decadal intervals. The predicted future LULC maps revealed the drastic expansion of built-up that these basins might witness in the coming decades. iii The NEX-GDDP and CMIP6 datasets, along with the projected LULC, are considered for impact assessments. The Soil and Water Assessment Tool (SWAT) is employed to simulate streamflow under LULC and climate change scenarios. The LULC 2050 scenario shows the most significant rise in average annual streamflow, at 5.9%, 6.7%, and 7.5%, respectively, in the Meenachil, Manimala, and Achencoil basins. The average monthly flow, as well as the extreme flow indices, is also expected to increase with the predicted LULC in the study basins. Meanwhile, in climate change scenarios, the response of the average annual flow varies according to the selected emission scenarios and time slices. It is observed that there is a decrease in average annual flow under RCP 4.5 and an increase under RCP 8.5. However, according to the SSP 126 and SSP 245 scenarios, the flow is projected to decrease in the near future and then increase towards the end of this century. The SSP 370 and SSP 585 showed an increase in flow in most of the time slices. The combined impacts of climate change and LULC change are found to be relatively higher than the isolated effect of these drivers in the basins. The study outcomes are expected to help policymakers in assessing the impact of climate change and LULC change on the hydrology of the river. This will enable the adoption of management measures that take the riverine ecosystem into account.Item Assessment of Future Transition in Climate Extremes Over Western Ghats ff india Using Machine Learning Based Multi-Model Ensemble Techniques(2024) SHETTY, SWATHI; U, PRUTHVIRAJBetter hazard management in the future requires a ramifications of climate change on water resources, with particular emphasis on the regional scale. The rapid urbanization and industrialization, combined with the drastic changes in the land use and land cover of the region over the Western Ghats (WG), have driven regional heterogeneity in the climate. Key factors such as rainfall, temperature, topography, and vegetation are crucial in unraveling the intricate interactions between ecosystems and climate systems. The comprehension of forthcoming variability in these factors holds significant importance for the region owing to its global significance. The future climate projections rely on the Global Circulation Models (GCMs) ; thus, it is crucial to make sure those GCMs are accurate representations of the current climate in the region. Therefore, the study aimed to a) understand the role of topographic structure on rainfall distribution and its association with topo-climatic variables, and the vegetation, b) rank the GCM models and examine the efficacy of advanced machine learning based ensemble techniques to capture the inter-seasonal temporal variability over diverse geo-climatic basins of ghat, c) examine the uncertainties in multi-model ensembles of GCMs to capture the extreme climate indices and their trend d) model the potential occurrence of severe minimum and maximum temperatures, rainfall events, potential evapotranspiration, and historical and projected trends and e) understand the impact of climate change on the future variability in the streamflow. The dependability of rainfall on the topography and climate of the region is evaluated using the Geographically Weighted Regression method. It is observed that the effect of the terrain is amplified in the broad, gradually sloping intermediate rough mountain located in close proximity to the coast. The maximum amount of rainfall is contingent upon the steepness of a mountain’s windward side and the topographic structure resulting in the difference in the elevation of maximum rainfall occurrence. Based on this, the six river basins located in the i diverse-geoclimate of theWestern Ghat are used to evaluate the performance of the GCMs and to understand the future variability in climate and the extremes. The top-performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using simple Arithmetic Mean (AM) and seven machine learning-based ensemble methods. Further, its ability to imitate extreme climatic events is analyzed using the indices formulated by the Expert Team on Climate Change Detection and Indices. Then the frequency and trend in the projected extremes of precipitation, minimum and maximum temperature, are obtained for the Shared Socioeconomic Pathways SSP245 and SSP585 for the Near Future (2021–2050) and Far Future (2051–2100) horizon. The streamflow of the river basins is simulated using Long Short Term Memory (LSTM), a deep learning technique to assess the potential impact of climate change on streamflow. The performance of individual GCM models varies in all the basins; also, the ability to imitate the observation varies with the climatic variables, with notable disparities in the simulation of climate patterns. The ensemble of top-performing models has been proven beneficial in river basin scale by overcoming the constraints of bias correction methods. The Multi-model ensemble (MME) of Extreme Gradient Boosting (XGBR) and Random Forest Regression stand out for their superior performance across all river basins, with exceptional performance over the per-humid basins, while Adaptive Boosting, Support Vector Regression, and the AM underperform. Despite excellent accuracy in predicting daily/monthly rainfall, still, a great deal of variability in calculating climatic indices is noted, with higher relative bias in precipitation indices. Except for the duration-based precipitation indices, the XGBR calculated indices have been shown to be more accurate across all basins. The anticipated fluctuations in temperature emphasize the onset of increased warming in November, which extends up to June, resulting in a notably warmer winter and an extended summer season. In future decades, warm days and nights increase by 45–65% and 45–70% in Aghanshini and northern river basins, and 45-85%, 60-80% in southern and Netravati river basins receptively, with two fold ii warming in the winter season. After the mid-21st century, the warming trends start to slow down with decreasing trends in the pre-monsoon maximum temperature in southern and central river basins and a decrease in the monsoon minimum temperature in the northern river basins. The June and July rainfall will be highly inconsistent in the future decades, with a substantial increase in very wet to extremely wet days and medium to heavy rainfall in northern river basins. The streamflow in the monsoon season decreases substantially, with a decrease in annual streamflow in Chaliyar and Netravati and converse in other river basins. The southern river basins and the Netravati river basins are extremely vulnerable to water scarcity risk in May and June months, which extends to April and July in the high emission scenarios. These findings serve as an indication of the range of anticipated changes in the magnitude of extreme maximum and minimum temperature, rainfall, and geographical pattern over the Western Ghats.Item Assessment of Meteorological and Hydrological Droughts Using Stationary and Non-Stationary Indices for two Contrasting Climate Regions in India(National Institute of Technology Karnataka, Surathkal, 2024) SAJEEV, ARYA; KUNDAPURA, SUBRAHMANYAOnly a few researchers have incorporated climate change in drought indices calculations. This research attempts to build non-stationary indices for assessing meteorological drought in two different climate zones of India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop non-stationary 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 non-stationary 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 analysed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The non-stationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The non-stationary drought index shows better consistency with historical drought and flood events than the stationary index. The impact of rainfall and drought on the yield of major crops in study areas is also analysed. The yield loss rate of bajra significantly correlates with Non-stationary Standardized Precipitation Index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. Co-occurrence 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. Drought forecasting is crucial in water resource management and agricultural planning, particularly in regions vulnerable to water scarcity. Hence, the efficacy of various time-series forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Feed-forward Neural Network (FNN), Recurrent Neural Network (RNN), as well as hybrid combinations such as ARIMA-FNN, ARIMA-RNN, FNN-ARIMA, and RNN-ARIMA, for predicting drought indices at different time scales (3, 6, 12, and 24 months) is performed in Saurashtra and Kutch. The effectiveness of the models is evaluated through Correlation Coefficient (CC), R-squared (R2), Mean ii Square Error (MSE), Mean Absolute Error (MAE), and Relative Absolute Error (RAE). FNN exhibits superior performance as a standalone model across all time scales considered, and scale 24 was the best-performing time scale with a Correlation Coefficient of 0.874 and R2 of 0.911. However, further improvements in forecast accuracy are observed at all time scales when incorporating ARIMA as a post-processing step in the hybrid FNN-ARIMA model. Notably, FNN-ARIMA emerges as the top-performing model among all evaluated approaches, demonstrating its effectiveness in capturing the complex temporal dynamics of drought phenomena. This research emphasizes the significance of hybrid forecasting techniques, especially the combination of neural networks with traditional time-series models, in enhancing drought prediction accuracy. The findings contribute to the advancement of forecasting methodologies for better-informed decision-making in water resource management and agricultural sectors, thereby aiding in mitigating the impacts of drought events on vulnerable regions. Comparative analyses of meteorological and hydrological droughts using non-stationary indices have not been explored yet. The other objective of this research is to develop non-stationary indices for assessing meteorological and hydrological droughts in the Shetrunji River basin in Saurashtra region, India, from 1971 to 2015. The statistical performance of stationary and non-stationary models has been compared across various time scales (3-,6-,12- and 24- months), and the results indicate that non-stationary models more effectively capture meteorological and hydrological drought events than stationary models. The drought and flood events detected by non-stationary indices are compared with historical episodes to assess the robustness of the indices. The results are also compared with drought events obtained from rainfall and streamflow departures. The annual and seasonal departures in rainfall and streamflow show the highest deficiency of rainfall and streamflow in 1987. The probability of different drought classes is calculated, and a higher likelihood of severe to extreme dry conditions is observed compared to very wet and extreme wet conditions in the basin. Investigation has been conducted on the impact of meteorological drought on hydrological drought and a correlation analysis between both types of droughts. A significant correlation is observed between meteorological and hydrological drought at iii all analysed time scales. Meteorological drought impacts surface water resources with a one-month lag at all time scales, with the highest response rate obtained at 6-month scale (91.13%). The study also examines the impact of drought on yield loss in Kharif (Bajra) and Rabi (Wheat) crops. Bajra and wheat yield loss rates strongly correlate with non-stationary drought indices, with a more significant effect of drought on bajra yield than wheat during major drought events. The hydrological drought analysis in the humid Netravathi River basin is also conducted using stationary and non-stationary indices. This drought analysis provides feasible results in both arid and humid regions in a changing environment. This novel dimension of drought studies provides practical insights into semi-arid regions in a changing environment. The findings can be utilized by various sectors, including drought management, agricultural planners, and policymakers, to reduce crop loss due to drought.Item MINERAL IDENTIFICATION On MARTIAN SURFACE USING SUPERVISED LEARNING APPROACH FROM CRISM HYPERSPECTRAL DATA(National Institute of Technology Karnataka, Surathkal, 2024) KUMARI, PRIYANKA; Shetty, Amba; Koolagudi, Shashidhar GThe availability of spectral libraries for CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) data through NASA’s Planetary Data System has revolutionized the study of the surface mineralogy of Mars. However, building supervised learning models for mineral mapping remains a challenge due to the scarcity of ground-truth training data. In this thesis, an innovative framework is presented that leverages supervised learning to classify spectra within CRISM hyperspectral images. To overcome the data limitation, an augmentation approach is employed that creates the training data by augmenting the minerals available in the MICA spectral library, preserving key absorption signatures of each mineral class while introducing adequate variability. The framework includes a comprehensive pre-processing pipeline, featuring a novel feature extraction method to capture distinctive absorption patterns in the spectra. The approach is validated using CRISM images from diverse Martian locations and interactive mineral maps are also provided for the detected dominant minerals. While this initial framework ensures acceptable accuracy, utilizing more sophisticated learning models and advanced preprocessing techniques can enhance the performance of the framework. Spectra in remotely sensed hyperspectral images are often affected by the presence of continuum, which changes the global curvature of the spectra, although the key absorption signatures are present. The continuum removal process, one of the critical preprocessing steps in the pipeline, is modified from the traditional approach to a novel method named Segmented Curve Fitting, which can identify more absorption shoulder points in a spectrum and thus can detect the absorption features in it more distinctively. Lastly, the thesis introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture tailored for mineral identification using CRISM hyperspectral data. Inspired by Inception-V3 and InceptionResnet-V1 architectures, MICAnet leverages 1-dimensional convolutions for processing spectra at the pixel level. This innovative architecture represents a significant contribution, being the first solely dedicated to this objective. The performance of the mineral mapping framework is assessed using both simulated data of varying complexity and a real CRISM TRDR/MTRDR hyperspectral dataset. In conclusion, this study advances the field of planetary science and remote sensing by providing automated approaches for mineral identification and mapping on Mars, also, enhances the understanding of Martian surface mineralogy, offering valuable insights into the planet’s geological history and habitability.Item Assessment of Drought Indices for the Meteorological Subdivisions of India Using Machine Learning Techniques(National Institute of Technology Karnataka, Surathkal, 2024) KIKON, AYILOBENI; DODAMANI, B MDrought is a highly damaging natural event having a significant impact on the environment, agriculture, economy and public health resulting in a cascade of vulnerabilities across several sectors. Drought occurs in all climatic zones mainly because of deficit in precipitation for a prolonged period. Every year significant areas and population around the globe are affected by drought which can last anywhere between weeks to years. Understanding the numerous climatic parameters affecting the variability of rainfall and outbreaks of drought is a major scientific challenge. Due to changes in the climate and activities by people, there is a need to understand the various catastrophe causing due to drought and adopt measures to overcome and prevent the drought consequences. Drought prediction emerges as one of the crucial tools that can provide helpful information and may be used to mitigate drought impacts. Meteorological drought is a type of drought that results from inadequate amounts of rainfall in any region. The study has been conducted in the Indian region consisting of thirty-four meteorological subdivisions. The study aims to analyse the rainfall and drought indices trend using the monthly precipitation data from 1958-2017. The Mann-Kendall test has been applied to determine the trends in rainfall and drought indices. The Effective Drought Index (EDI) and Standardized Precipitation Index with 9-month and 12-month timescale are the meteorological drought indices that are assessed using monthly rainfall data. These meteorological drought indices are predicted using machine learning algorithms such as the Genetic Algorithm-Adaptive Neural Fuzzy Inference System (GA-ANFIS), Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System (PSO-ANFIS), and Generalized Regression Neural Network (GRNN), and the obtained results are compared. The Mann-Kendall test results showed a clear indication that rainfall has been consistently decreasing during the study period, leading to water shortages and dry conditions. Understanding both rainfall patterns and drought trends is therefore essential for efficient planning and control of the numerous impacts of drought. The ii machine learning algorithms employed in this work show they are capable of predicting meteorological drought indices under various climatic situations. Based on performance measures such as coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Normalized root mean square error (NRMSE), comparative study of the models shows that hybrid machine learning models (GA-ANFIS and PSO-ANFIS) perform better than the non-hybrid model (GRNN). Notably, it has been observed that, as the timescale for the drought index increases, it shows a better performance with more accuracy of the performance metrics. Based on the study findings, it emphasizes in assessing the rainfall and drought trend could be beneficial in understanding the drought behaviour and identify drought prone locations and develop mitigation strategies to overcome the drought impacts. Overall, this study plays a significant role in understanding the rainfall pattern and its distribution for water management and planning for future water use. Adopting hybrid machine learning algorithms for predicting of meteorological drought indices may provide a better outcome for drought assessment. Also, assessing the historical droughts provides a better understanding and management of past drought occurrences. Future research attempts could be focused on improving drought vulnerability mapping by modelling and probabilistic climate data analysis. Additionally, understanding the dynamics of drought may also be improved by investigating at how drought occurrences begin and terminate. The exploration of alternative hybrid machine learning approaches and the incorporation of additional drought indices could contribute to more robust evaluations in assessing drought conditions.Item Advanced Spectral Spatial Approaches for Dimensionality Reduction of Hyperspectral Data(National Institute of Technology Karnataka, Surathkal, 2024) C, DEEPA; SHETTY, AMBA; NARASIMHADHAN, A.V.Recent advances in sensor technology have enabled the collection of large data in hyperspectral remote sensing. Although rich spectral information is captured in hundreds of narrow contiguous bands, the hyperspectral data possess several limitations such as mixed pixels, high intraclass variability, interclass similarity, and the curse of dimensionality which restricts the potential of conventional machine learning classifiers. Dimensionality reduction (DR) and incorporation of spatial information can be taken into account to increase the interpretability of hyperspectral data. The thesis mainly focuses on the implementation of different approaches for DR of hyperspectral data to address the curse of dimensionality, limited samples and labelled data issues inherent in hyperspectral data. First, a quality measure based on the co-ranking matrix has been proposed for the performance evaluation of 15 DR techniques for mineral exploration. The selection of appropriate techniques for a particular task is challenging due to the diversity and ever-increasing number of DR techniques. A few important aspects in this regard have been explored in detail. Clustering is performed using the K-means algorithm and the relationship between the quality index and clustering accuracy has been examined concurrently for the first time in hyperspectral remote sensing. Furthermore, the loss of quality in the process of DR has also been analyzed which provides sufficient input for the end-user to select an appropriate DR technique. Second, the ability of the Convolutional Neural Network (CNN) for supervised learning of hyperspectral data is explored. A fast and compact hybrid CNN which combines the strengths of 3D and 2D convolutions to extract joint spectral-spatial information has been proposed to analyze the impact of different feature extraction techniques on classification performance. The effect of input patch size on final results has been well demonstrated. A detailed investigation of classification accuracy, execution time, and comparison with nine state-of-the-art approaches has been demonstrated. ii Next, a novel deep feature selection strategy using autoencoders inspired by knowledge distillation has been implemented for the model compression and selection of informative bands. The potential of convolutional autoencoders has been well explored in selecting discriminative bands. Sensitivity analysis tests and different applications have been considered to verify the generalization capability of the proposed model. The potential of unsupervised learning schemes has been discussed in detail. Finally, a generator model based on Generative Adversarial Networks (GAN) has been proposed for virtual sample generation and compact representation of hyperspectral data. The training instability issue in Vanilla GAN has been addressed by the effective implementation of deep convolutional GANs. By comparing the spectra of the generated hyperspectral images to the corresponding real ones, the quality of the images is assessed. The potential of augmented data for improvement in classification accuracy has also been investigated.