Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Author: search.filters.author.Venkatesh, V.

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    Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models
    (Elsevier B.V., 2022) Sharannya, S.; Venkatesh, V.; Mahesha, M.; Acharya, T.D.
    Study region: Varahi River originating from the Western Ghats of India. Study focus: We developed a hybrid model that integrates process-based hydrological model (PHM) and data-driven (DD) techniques to generate streamflow simulations precisely. The hybrid modeling framework is practical as it respects hydrological processes through the PHM while considering the advantage of the DD model's ability to simulate the complex relationship between residuals and input variables. Further, we have proposed an optimization-based nudging scheme for post-processing the hybrid model simulated streamflow to overcome the limitations in PHM and DD. New hydrological insights for the region: We formulated two approaches for simulating streamflow ensembles using DD and PHM models. In approach− 1, DD models are initially used to ensemble meteorological variables and then use the ensembles in a PHM to simulate streamflows. In approach− 2, PHM is forced with different sets of meteorological variables to simulate multiple streamflow sets and then use DD models to ensemble the PHM-derived streamflows. Random forest exhibited better performance for ensembling precipitation, temperature, and streamflow datasets compared to the other five DD algorithms in the study. Streamflows generated using approach− 2 showed reliable estimates when compared against observed streamflow values. However, post-processing the hybrid streamflows using an optimization-based nudging scheme outperformed the streamflows generated in approach− 1 and approach− 2 with better model fit statistics (R2 and NSE of 0.69 and 0.66). The output from the nudging scheme was further utilized for streamflow predictions under the combined impact of land use/cover (LULC) and climate change (CC) under the Representative Concentration Pathway 4.5 scenario. It depicted a decrease in monthly and seasonal stream flows with − 22.65 %, − 31.77 %, − 11.81 % for winter, summer, and monsoon seasons, respectively. These results suggest that water availability will decline, and water scarcity will increase in the study region. These variations in streamflow might negatively impact agriculture and natural ecosystems and even lead to water restrictions in the region. © 2022 The Authors
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    Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images
    (Springer, 2023) Agrawal, S.; Venkatesh, V.; Nara, M.; Patil, N.
    COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50’s abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model’s explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model’s ability to generalize. The proposed model is intended to be deployed for free use. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.