shailesh kumar

@gecbuxar.ac.in

Assistant professor in Civil engineering
Government engineering college,Buxar

shailesh kumar


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https://researchid.co/shailesh456

RESEARCH, TEACHING, or OTHER INTERESTS

Civil and Structural Engineering, Multidisciplinary

FUTURE PROJECTS

HYDROLOGICAL STUDY


Applications Invited
2

Scopus Publications

11

Scholar Citations

2

Scholar h-index

Scopus Publications

  • Comparison of the performance of SWAT and hybrid M5P tree models in rainfall–runoff simulation
    Shailesh Kumar, K. K. Pandey, Ajay Ahirwar
    Journal of Water and Health, 2024
    Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model's performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R2), Nash–Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks.
  • Estimating rainfall-runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm
    Shailesh Kumar, K. K. Pandey, Sunil Kumar, Sunidhi Supriya
    Journal of Hydroinformatics, 2022
    Rainfall–runoff modeling is one of the most well-known applications of hydrology. The goal of rainfall–runoff modeling is to simulate the peak river flow caused by an actual or hypothetical rainfall force. In existing methods, the rainfall–runoff relationships are quantified to predict the daily streamflow of each catchment from its landscape attributes to measure the daily rainfall. However, the structural model error, infiltration rate, and the steep slopes of the hill affect the prediction process. To tackle these issues, this paper proposed a novel rainfall prognostic model-based artificial framework, which predicts day-to-day rainfall to prevent environmental disasters. The day-to-day predictions minimize the risks to life and property and also manage the agricultural farms in a better way because the possibility of rainfall has been estimated earlier. Furthermore, the posterior fire-breathing network is utilized to estimate model errors in the computational runoff by using time-dependent and random noise to the model's internal storage to solve the uncertainty problem. Since the model errors are estimated, there are limits to the infiltration rate and thus a prophetic multilayer network is utilized which relies on the soil runoff levels. Moreover, the network measures the dynamics of soil moisture to regulate the infiltration rate according to the rural or urban section. Moreover, to measure the surface water from the steep slopes, the system offered a well-ordered selective genetic algorithm to calculate the velocity of runoff in different bend areas to overcome the numerical problem. Thus, the model results showed that the work effectively predicts the rainfall from the investigation of model errors, infiltration rates, and velocity to achieve a better prediction range in the rainfall.

RECENT SCHOLAR PUBLICATIONS

  • Comparison Of The Performance Of HEC-HMS And RF Models Models In Rainfall Runoff Simulation Authors Dr. Shailesh Kumar
    DS kumar
    https://theaspd.com/index.php 11 (https://theaspd.com/index.php/ijes/issue) , 2025
    2025.0
  • Rainfall–Runoff Modelling Using Hydrological Modelling And Soft Computing Techniques
    DS kumar
    https://theaspd.com/index.php/ijes/article/view/6207 , 2025
    2025.0
  • Comparison of the performance of SWAT and hybrid M5P tree models in rainfall–runoff simulation
    S Kumar, KK Pandey, A Ahirwar
    Journal of Water and Health 22 (4), 639-651 , 2024
    2024.0
    Citations: 6
  • Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm
    S Kumar, KK Pandey, S Kumar, S Supriya
    Journal of Hydroinformatics 24 (5), 1066-1090 , 2022
    2022.0
    Citations: 3
  • Monitoring of two typical glacier lakes in Indus Basin
    S Kumar, NK Goel
    Int. J. Eng. Res. Technol 3, 1-7 , 2015
    2015.0
    Citations: 2
  • Rainfall-Runoff Simulation And Modeling Using HEC-HMS Model
  • A Study of Effectiveness of Biological Methods in Water Treatment Process with respect to a Conventional Method. water resources, 6069, 6083.
    MKM Rajnish Kumar Upadhyay
    African journal of biological science, (AFJBS) ISSN: 2663-2187 , 0
  • Flood estimation of Gangabal Lake in Indus River Basin using MIKE 11
    S Kumar, NK Goelb, S Supriyac
    EDITORIAL BOARD, 285 , 0

MOST CITED SCHOLAR PUBLICATIONS

  • Comparison of the performance of SWAT and hybrid M5P tree models in rainfall–runoff simulation
    S Kumar, KK Pandey, A Ahirwar
    Journal of Water and Health 22 (4), 639-651 , 2024
    2024.0
    Citations: 6
  • Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm
    S Kumar, KK Pandey, S Kumar, S Supriya
    Journal of Hydroinformatics 24 (5), 1066-1090 , 2022
    2022.0
    Citations: 3
  • Monitoring of two typical glacier lakes in Indus Basin
    S Kumar, NK Goel
    Int. J. Eng. Res. Technol 3, 1-7 , 2015
    2015.0
    Citations: 2
  • Comparison Of The Performance Of HEC-HMS And RF Models Models In Rainfall Runoff Simulation Authors Dr. Shailesh Kumar
    DS kumar
    https://theaspd.com/index.php 11 (https://theaspd.com/index.php/ijes/issue) , 2025
    2025.0
  • Rainfall–Runoff Modelling Using Hydrological Modelling And Soft Computing Techniques
    DS kumar
    https://theaspd.com/index.php/ijes/article/view/6207 , 2025
    2025.0
  • Rainfall-Runoff Simulation And Modeling Using HEC-HMS Model
  • A Study of Effectiveness of Biological Methods in Water Treatment Process with respect to a Conventional Method. water resources, 6069, 6083.
    MKM Rajnish Kumar Upadhyay
    African journal of biological science, (AFJBS) ISSN: 2663-2187 , 0
  • Flood estimation of Gangabal Lake in Indus River Basin using MIKE 11
    S Kumar, NK Goelb, S Supriyac
    EDITORIAL BOARD, 285 , 0