1. Ph.D Theses
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/11
Browse
4 results
Search Results
Item Hydrological Impact of Land Use and Climate Change on The West Coast River Basins of Karnataka(National Institute Of Technology Karnataka Surathkal, 2023) T.M, Sharannya; Mahesha, AmaiThe Western Ghats of India is an environmental and climate-sensitive region of India. The Western Ghats are the mountainous forest range of a tropical region that play significant role in distributing Indian monsoon rains. Three west-flowing rivers of the Western Ghats representing different levels of anthropogenic influence were chosen for this study to understand the individual and combined effect of land use land cover (LULC) and climate change (CC) on the hydrology of river basins that spread over the northern, middle and southern portion of the west coast Karnataka. The study was carried out with five objectives which include (i) Assessment of satellite and India Meteorological Department (IMD) rainfall products for streamflow simulation in the study area, (ii) To investigate long-term changes in current LULC and model predicted future LULC scenarios on streamflow, (iii) To evaluate the impact of long-term climate change on regional hydrology using SWAT and to assess the river basin responses, (iv) To assess the combined impact of land use land cover change and climate change over the study area, (v) Scenario analysis of the combined effect of land-use change and climate change on blue water and green water availability. Evaluation of satellite precipitation data was performed using the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset. The historical land use (LU) changes were studied for four decades (1988– 2016) using the maximum likelihood algorithm and the long-term LU (2016–2100) was estimated using the Dyna-CLUE prediction model. Five General Circulation Models (GCMs) were utilized to assess the effects of climate change (CC) and the SWAT model was used for hydrological modeling of the three river basins. To characterize granular effects of LU and CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006–2040), mid-future (2041–2070), and far future (2071–2100) based on climate were established. iIt was observed that the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The impact of climate change was more predominant than the impact due to land use land cover. However, deforestation and the conversion of other LULC into an unorganized plantation/ agriculture with urban expansion contribute to an increase in streamflow. As per the water availability and vulnerability assessment, the Aghanashini basin was classified under the extremely vulnerable sector, Gurupura and Varahi basins under the low vulnerable sector for water scarcity. The thesis is an attempt to study the LULC comprehensively on the impact on rivers of the Western Ghats of India and is an effective tool in understanding the hydrological impacts and adopting strategies to counter the impacts of LULC and CC.Item Plant Disease Detection Using Deep Learning-Based Approach(National Institute Of Technology Karnataka Surathkal, 2023) C K, Sunil; C D, Jaidhar; Patil, NagammaFood security is threatening due to the exponentially growing global population. There are many reasons for food scarcity, such as exponential population, environmental dis- asters, climate change, the impact of COVID-19, and wars. Agriculture’s productivity has decreased in the last decade due to climate change and inappropriate usage of wa- ter, fertilizer, and pesticides, which stimulate plant diseases. Plant diseases and pests are also the cause of reducing the production of food all over the globe. Plant diseases cause around 20% to 40% loss in the production of agricultural products. Plant diseases extensively impact agrarian production growth. It results in a price hike on food grains and vegetables. Early detection of plant disease is essential to reduce economic loss and predict yield loss. Early perception of pathogens and insinuating proper medications are crucial to enhance crop yield and quality. Current plant disease detection involves the physical presence of domain experts to ascertain the disease. As a result, timely plant disease recognition entails sustained crop supervision from the start. Some research works have contemporarily been proposed as curative control measures. However, such an approach requires expensive equipment that is out of reach for small or middle-scale yeoman. Deep learning-based approaches vary in network architecture, and learning of the features by each model varies from one another in some aspects. To take this as an ad- vantage, this study proposed an ensemble-based deep learning approach using AlexNet, ResNet, and VGGNet. Seven different plant disease dataset is used with the binary and multiclass dataset. This ensemble-based approach enhances the classification result by minimizing the miss-classification effect. It constructively perceives plant diseases by analyzing plant leaf images. A broad set of experiments were conducted using differ- ent plant leaf image datasets such as Cardamom, Cherry, Grape, Maize, Pepper, Potato, and Strawberry to assess the agility of the proposed approach. Experiential results show that the proposed method attained a maximal detection accuracy of 100% for binary and 99.53% for multiclass datasets. Deep learning-based plant disease detection is proposed in this work by address- ing some of the challenges. Precise plant disease detection is essential, where more than one disease has similar symptoms and nature, and also to achieve excellent per- formance in spite of the imbalanced data. This study proposed a Multilevel Feature Fusion Network (MFFN), which combines the features learned at different levels of the network and also uses the adaptive attention technique by employing channel and pixel attention mechanism, which fabricates the network more robust by considering the ideeper network features which are shown in different channels and also with the pixel level features, with this the network is able to classify the diseases precisely on tomato plant dataset. The proposed deep learning-based approach is trained and tested on a tomato plant leaves dataset and achieved 99.36% training accuracy, 99.88% validation accuracy, and 99.5% external testing accuracy. It outperformed the existing approaches relevant to the tomato plant dataset. Further, this work also proposes a pesticide pre- scription module that provides pesticide information based on the type of tomato leaf disease. Plant disease detection using a complex background and images captured in differ- ent conditions is one of the challenges; this study proposed a cardamom plant disease detection approach by collecting the images in a complex background using different electronic gadgets. This study proposed a hybrid deep learning-based approach consist- ing of two stages: the background removal stage and the classification stage. U2 -Net is used for the background removal task, and EfficientNetV2 is used for the classifica- tion task. This makes the network more robust to handle the plant leaf images captured in complex nature.A large number of experiments were conducted to evaluate the pro- posed approach’s performance and compare it to other models such as EfficientNet and Convolutional Neural Network (CNN). According to the experimental results, the pro- posed approach achieved a detection accuracy of 98.26%. The approaches proposed in this study are producing prominent results. This study also suggested a pesticide prescription module for tomato plant leaf diseases. The pro- posed solutions in this study contribute to the field of plant disease detection, which can be adopted for real-time plant disease application. The overall aim of this study is to provide an efficient and robust plant disease detection approach.Item Numerical Model Studies to Predict the Wind-Wave Climate Considering Climate Change Effects(National Institute of Technology Karnataka, Surathkal, 2021) K, Sandesh Upadhyaya.; Rao, Subba; ManuThe waves propagating over an area under the action of the wind is termed as wind waves. The disturbances on the ocean surface by the wind are restored to a calm equilibrium position by the action of gravity. The fundamental element in the wind-wave generation is the interaction between air and ocean. During this interaction, there is an energy and momentum transfer between the atmosphere and ocean. The climate change affects the atmospheric temperature which in turn alters the wind patterns. The wave conditions change according to the wind pattern. Studies on global climate changes and extreme weather events have fascinated researches all over the world. Climate change, a global phenomenon, is a consequence of ever-increasing greenhouse gas concentration and is considered a serious threat to mankind. Climate change is a phenomenon triggered by natural and anthropogenic activities, which is one of the most discussed topics in the research community today. An increase in global sea level, changes in wind pattern and an increase in the frequency of extreme wave events which is caused by climate change have critical impacts on the coastal population around the world. Indian coast measures about 7500 km along with the nine coastal states which host marine and coastal biodiversity. Thirteen major ports and associated activities play a prominent role in coastal population concentration of about 14% along the Indian coast. The coastal and offshore structures are typically designed for the significant wave height (HS) corresponding to a specific return period and it is, therefore, necessary to know possible changes in their magnitudes at different locations of interest. Structures built in the sea are traditionally designed according to historical climate observations or hindcasted data. For structural safety, consideration of such climate change effects is highly desirable. Computational advancements in recent times have resulted in various General Circulation Models being developed and effectively used for assessing the atmospheric and ocean circulation. The performance of these modelled result can be compared with the in-situ measurements of shorter duration. Forecast of the climate parameters incorporating climate change effects are developed. These data products can be used to develop numerical wave models for long term analysis of wind and wave patterns which will aid in the design of coastal and offshore structures. i i In the present study, hindcasting from 1980 for the Indian domain is performed from reanalysed gridded global wind speed dataset called ERA-Interim. The performance of this global dataset is assessed by comparing it with in-situ measurements recorded at the east and west coast of India. As the ERA-Interim dataset showed a good match with the in-situ records these long-term wind speeds are used as an input to the numerical wave model. MIKE 21 SW numerical wave model is developed for the Indian domain with coordinates - 4º to 30º N 40º to 95ºE. Significant wave heights from this wave model driven by ERA-Interim wind speeds are extracted at locations nearshore to Karwar and offshore OB03 location for validation. After validation, the numerical model is used to perform longterm wave analysis, shoreline analysis, assessment of wind-wave climate along the Indian coast and wave climate predictions along Karnataka coast for the near future. The numerical model output depends on the input which is global wind speed dataset. Wind speed analysis is initially performed before using it in the numerical model. As ERA-Interim dataset does not provide forecasts, global wind speeds provided by the CMIP5 database is considered in this study. Wind speed projections from 38 different CMIP5 global models are compared against ERA-Interim global wind speeds for the Indian domain. The performance of datasets is graphically evaluated based on Taylor plots. Initially, statistical analysis of monthly wind speeds from 1980 to 2005 is performed to arrive at four best performing datasets for the Indian domain. Further, a nowcast study on daily wind speeds from 2006 to 2018 considering the four climate change scenarios termed as Representative Concentration Pathways (RCPs) is carried out. From the nowcast analysis, an Italian CMIP5 dataset called CMCC-CM for RCP 4.5 matched well with the real-time reanalysed wind speeds provided by ERA-Interim. Hence in the present study, wave climate predictions for the Indian domain is based on wind speeds driven by CMCC-CM RCP 4.5. The long-term analysis is performed based on the five probability distributions such as Log-normal distribution, Gumbel distribution, Fretchet distribution, Exponential distribution, and Weibull distributions to arrive at significant wave height with 10 and 50 year return period for New Mangaluru port location. Initially, long-term analysis is performed on in-situ records measured for 5 years near New Mangaluru Port. From this analysis, Weibull distribution with α=1.3 showed good performance and is used to arrive at significant wave heights with 10 and 50 year return period. The same approach is extended on the MIKE 21 simulated significant wave heights from 38-year ERA-Interim hindcast. The results showed 2.6% and 5.44% increase in significant wave height with 10 year and 50 year return period at the location studied. ii i A shoreline analysis is performed using LITPACK tool along the coast adjacent to the New Mangaluru Port. The volume of sediment transport is analysed and the shoreline changes from 1980 to 2015 is studied to understand the erosion and accretion patterns. The performance of the numerical model matched well with the satellite measurements. In an attempt to explore the renewable energy potential along the Indian coast the numerical wave model is also used to assess the wind-wave climate based on ERA-Interim wind speed data of 38 years. The results showed amongst the locations studied off Goa, Karnataka, Kerala, Tamil Nadu, and Andhra Pradesh had good potential to extract offshore wind energy from offshore wind turbines. MIKE numerical model driven by wind speeds from CMCC-CM RCP 4.5 up to the year 2070 is used to simulate the wave climate along the Karnataka coast. The monsoon wave climate is studied to arrive at wave parameters with 10 and 50 year return period at six locations along the Karnataka coast.Item Simulation of Hydrological Effects of Land Use/ Land Cover, Climate Change, and Effect of Dam at Gilgel Abay River Basin, Ethiopia(National Institute of Technology Karnataka, Surathkal, 2016) Adal, Arega Mulu; Dwarakish, G. S.Water is the most essential resource for survival of living things and it is the most crucial resource associated with land use/ land cover (LU/LC) and climate changes. Hence, it is very important to make evaluations of the expected impact on the hydrology and water resources. Flood is the most chronic and hazardous phenomena all over the world and causes loss of human life, natural resources as well as infrastructures. In addition, dams have been designed and constructed for various purposes. However, dams have effects on water and sediment transport, which determines overall morphology of river. Ethiopia has many dams; one of these dams is Koga dam which was constructed across Koga River, which is tributary to Gilgel Abay River, but information on effects on river hydrology and sediment transport was not evaluated. Therefore, this research was conducted at Gilgel Abay River Basin to address the following objectives; (1) to develop a hydrological model to evaluate the effect of land use/ land cover and climate change over the years on stream flow in the river basin, (2) to simulate stream flow to Lake Tana (3) to estimate future daily annual peak stream flow and flood frequency, and (4) to identify effect of dam on river hydrology and sediment transport. Precipitation Runoff Modeling System (PRMS), which is a modular-design, deterministic, distributed-parametric modelling system was used to evaluate the impacts of various combinations of precipitation, climate, and land use changes on stream flow as well as for predicting future annual daily peak stream flow. System inputs are daily time-series values of precipitation, minimum and maximum air temperature, and parameter files which are generated from Geographical Information System Weasel (GIS Weasel). The methods which were used to evaluate combined effects of LU/LC, vegetation type, vegetation density and climate changes on stream flow were two different periods` LU/LC, vegetation type, vegetation density and climate changes, these were: period one (1990-2000) and period two (2001-2010) of LU/LC, vegetation type, vegetation density and climate changes. These gridded maps as well as soil maps were used in GIS Weasel to generate parameters for PRMS model. Hence, these generated parameters within different time series data fed to PRMS model to simulate stream flow. To estimateii future daily annual peak stream flow and flood frequency, the values for historical climate changes in the basin were adjusted on the basis of changes that are projected for 21st century at Gilgel Abay river basin. Air temperature was adjusted by temperature values of no change, +1.50c and +30c of historical temperatures by adjusting model parameters rather than adjusting input variables. Precipitation was adjusted by two different precipitation values ranging from -10% to 10% of observed precipitation by adjusting input variables. In addition, Effect of Koga dam on river hydrology and sediment transport was evaluated by using hydrograph variations before and after the construction of dam as well as sediment yield at the catchment outlet of Koga river basin before and after the construction of dam by using Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Ratio (SDR). As climate and LU/LC, vegetation type and vegetation density changed from period one to period two, stream flow increased by 13.5% and ET decreased by 18.3% compared to baseline period (1993-2000). Future annual daily peak stream flow with 50% and 1% AEPs will increase by 14.3% of historical modeled value of peak stream flow at the end of 21st century when temperature is held constant and precipitation increases by 10%, but for other combinations, there is a decrease of stream flow. There is reduction of 5.9 t/year of sediment yield at the outlet of Koga river due to the construction of Koga dam. Generally, combined effects of LU/LC and climate change are more on stream flow and ET than individual effects, and Future annual daily maximum peak stream flow and flood frequency will decrease by large amount as temperature increases. In addition, construction of dam has an effect on river hydrology and sediment transport.