Conference Papers
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Item Multi-time instant probabilistic PV generation forecasting using quantile regression forests(Institute of Electrical and Electronics Engineers Inc., 2020) Tripathy, D.S.; Prusty, B.; Jena, D.; Sahu, M.K.Long-term planning for the reinforcement of power systems with PV-integration requires multi-time instant PV generation uncertainty modeling. Probabilistic forecasting of PV generation plays a vital role in the uncertainty management in power systems with PV penetration. An ensemble approach for probabilistic PV generation forecasting, such as the quantile regression forests, proves to be a suitable model because it models the uncertain PV generation more accurately compared to single mean models. The inherent nature of forests to prevent over-fitting by "bagging" the training data is an advantage. Also, the optimal choice of the model hyper-parameters adds to its efficiency as a forecaster. Further, the stochastic nature of weather conditions needs the selection of sensible regressors for the proposed quantile regression forests framework based on the physics of the underlying phenomenon. Real-world data for PV generation collected at multiple instants of time from the USA are employed to test the efficacy of the proposed probabilistic forecasting. The proposed model is compared against the basic quantile regression approach in terms of the accuracy of the quantile forecasts as well as prediction intervals using suitable scores and error metrics. © 2020 IEEE.Item A Review: Contribution of HEC-HMS Model(Springer Science and Business Media Deutschland GmbH, 2023) Sahu, M.K.; Shwetha, H.R.; Dwarakish, G.S.The rapid increase of population worldwide, urbanization, and industrialization significantly impact hydrologic processes locally and globally. Thus, development planning and managing various water resources are required to meet multiple water demands. However, acquiring gauge discharge data has always been difficult since measurements cannot be taken at every point along the river. Thus, HEC-HMS (Hydrologic Modeling System) is the hydrological model that can transform rainfall into a runoff by using known parameters, data, and appropriate mathematical equations to simulate flow records at the desired location. HEC-HMS was developed by the USACE and is freely accessible. It can estimate runoff from rainfall. In this paper, we review the studies carried out by researchers on the HEC-HMS model worldwide to ascertain its ability to simulate runoff with accuracy and use for making decisions. It could be seen that many researchers compared different modelling methods to obtain the best model suitable under different hydrological conditions and found HEC-HMS as a good model over others and recommended it for simulation of runoff. The reviews show that the HEC-HMS rainfall-runoff model has many flood modelling and water resource planning and management applications. In most studies, HEC-HMS rainfall-runoff modelling was found to be efficient and dependable in predicting runoff accuracy in various river basins. As a result, the model can simulate runoff in an ungauged basin for water resource planning, development, management, and decision-making. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Flood Inundation Mapping of Krishnaraja Nagar, Mysore Using Sentinel-1 Sar Images(Springer Science and Business Media Deutschland GmbH, 2024) Sahu, M.K.; Shwetha, H.R.; Dwarakish, G.S.Floods cause physical damage and impact the availability of food, water, and crops. Effective disaster management and disaster risk reduction strategies require a quick and accurate mapping of these phenomena. The study area selected is the Krishnaraja Nagar taluk, Mysore districts, Karnataka having an area of 608 Km2. In this study, the analysis of a flood event was conducted using the temporal GRDH SAR pictures in C-band from Sentinel-1. Additionally, the co-polarized Vertical transmit, and Vertical received (VV) Synthetic Aperture Radar (SAR) images were utilized to map the extent of the flooded area. Two methods of change detection are applied to the temporal SAR images: Otsu's Automatic thresholding method using Matlab R2020a, utilizing a pre-flood image dated 02 August 2018 that shares identical image characteristics with the flood images captured on 14 August 2018; and flood mapping based on Normalized Difference Flood Index (NDFI) using Sentinel Application Platform (SNAP) software. By dividing the SAR image's non-water and open-water regions, the threshold approach was used to extract the flooded areas. In order to identify the actual flooded region, permanent water bodies were later removed from the open water. An analysis of the overlay flood maps was conducted to determine the total area inundated. After processing the SAR data and conducting threshold operations, the flooded area estimates from NDFI is 28.10 km2, and by Otsu's method flooded area is 21.92 km2. It is concluded from the study that the SAR information, sideways with GIS, can be used efficiently for floodwater plotting, real-time analysis, and analysing the spread of floodwater in a flood-prone zone. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Flood Modelling and Mapping of Harangi River, Tributary of Cauvery River(Springer Science and Business Media Deutschland GmbH, 2024) Sahu, M.K.; Shwetha, H.R.; Dwarakish, G.S.Identifying and mapping the flood-prone area is a vital element of any flood management programme. Hydraulic modelling and remote sensing have been used for decades to predict flood events. In this study, unsteady flow analyses have been performed using the Hydrologic Engineering Centre-River Analysis System (HEC-RAS) software. The geometry file is created using the RAS Mapper tool. The study area selected is a 68 km stretch of the Harangi River from Kudige (12° 31′N, 75° 57′E) to Chunchunkatte (12° 30′24′′N, 76° 18′0′′E) gauging station in Karnataka. The required discharge data is collected from Central Water Commission, Bangalore. Manning’s roughness coefficient (n) is used as a simulating parameter to perform inundation mapping for the years 2018 and 2019, as the discharge in the river is high (2435, 2297 m3/s). Gumbel, Log-Pearson Type-3 (LP3) and Log-Normal (LN) distributions have been used to calculate peak discharges with return periods of 5, 10, 25, 50 and 100 years. The calibration and validation of the model is carried out by using data of simulated and observed discharge at the Chunchunkatte gauging station, which shows that the model developed in the present study is accurate. The result of the study shows that Manning’s n ranges between 0.003 and 0.005. For n = 0.005, the performance indices NSE, RMSE and R2 during calibration for the year 2018 are 0.663, 397.061 m3/s and 0.896; validation for the year 2019 is 0.72, 346.621 m3/s and 0.914; and the peak discharge for 100 years return period is 3419.48 m3/s via Gumbel distribution. The output of this study could be useful for flood control authorities to take necessary actions to prevent losses due to floods in the area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Streamflow Estimation for Harangi River Basin, Karnataka, India(Springer Science and Business Media Deutschland GmbH, 2025) Sahu, M.K.; Shwetha, H.R.; Dwarakish, G.S.Millions of people worldwide spend their entire day looking for water because it is so scarce. Therefore, conserving water is crucial, and it is believed that accurate estimation collection of runoffs is the first step to provide solutions to save water. This study used the Hydrologic Engineering Centre—Hydrologic Modelling System (HEC-HMS) model to simulate the Harangi River basin's reframed rainfall-runoff process (Area = 1703.0421 Km2) located in Kodagu District, Karnataka, India. The rainfall-runoff data were collected from 1995 to 2020 out of which five rainfall-runoff events were selected randomly for the study, three of these were designated for calibration, and the other two remained selected for validation. The Muskingum routing method was employed alongside the Soil Conservation Service—Curve Number (SCS CN) and the Soil Conservation Service Unit Hydrograph (SCS UH) to analyze and determine runoff characteristics. This included estimating runoff volume, peak runoff rate, and conducting flow routing assessments. The evaluation of the model's performance was conducted based on several criteria, including Nash Sutcliffe Efficiency (NSE), coefficient of determination (R2), and root mean square error (RMSE). The outcomes demonstrated that the model functions effectively during both the validation and calibration phases (NSE = 0.895, R2 = 0.948, RMSE = 346.435 m3/s; NSE = 0.887, R2 = 0.917, RMSE = 131.476 m3/s). Thus, the model can be used to manage different flood events and adopt effective decision and warning systems. Furthermore, other catchments with similar hydrological characteristics can use the created models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.