Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Rishith Kumar Vogeti"'
Autor:
Rishith Kumar Vogeti, Rahul Jauhari, Bhavesh Rahul Mishra, K. Srinivasa Raju, D. Nagesh Kumar
Publikováno v:
Journal of Water and Climate Change, Vol 15, Iss 2, Pp 832-848 (2024)
The present study analyzes the capability of convolutional neural network (CNN), long short-term memory (LSTM), CNN-LSTM, fuzzy CNN, fuzzy LSTM, and fuzzy CNN-LSTM to mimic streamflow for Lower Godavari Basin, India. Kling–Gupta efficiency (KGE) wa
Externí odkaz:
https://doaj.org/article/44716bc3c3cb450c85b3a65366fff110
Autor:
Bhavesh Rahul Mishra, Rishith Kumar Vogeti, Rahul Jauhari, K. Srinivasa Raju, D. Nagesh Kumar
Publikováno v:
Water Science and Technology, Vol 89, Iss 3, Pp 613-634 (2024)
The present study investigates the ability of five boosting algorithms, namely Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), Natural Gradient Boosting (NGBoost), and eXtreme Gradient Boosting (XGBoo
Externí odkaz:
https://doaj.org/article/cb0fd0fc262143b1a26ebf26a89cea5c
Autor:
Rishith Kumar Vogeti, K. Srinivasa Raju, D. Nagesh Kumar, Advani Manish Rajesh, S. V. Somanath Kumar, Yashraj Santosh Kumar Jha
Publikováno v:
Journal of Water and Climate Change, Vol 14, Iss 9, Pp 3150-3165 (2023)
Soil Water Assessment Tool (SWAT), Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and Hydrologic Simulation Program Fortran (HSPF) are explored for streamflow simulation of Lower Godavari Basin, India. The simulating ability of
Externí odkaz:
https://doaj.org/article/61e5406602494119b83aeb10bc442c03
Publikováno v:
H2Open Journal, Vol 5, Iss 4, Pp 670-685 (2022)
The present study applies three Machine Learning Algorithms, namely, Bi-directional Long Short-Term Memory (Bi-LSTM), Wavelet Neural Network (WNN), and eXtreme Gradient Boosting (XGBoost), to assess their suitability for streamflow projections of the
Externí odkaz:
https://doaj.org/article/2a9a3a2e2d0e48fb89a5b5171552c6c0
Publikováno v:
Journal of Water and Climate Change, Vol 13, Iss 11, Pp 3934-3950 (2022)
The present work aims to identify the best hydrological model structure suitable for the Lower Godavari River Basin, India, that forecasts streamflows. An extended version of the Framework for Understanding Structural Errors (FUSE), termed E-FUSE, is
Externí odkaz:
https://doaj.org/article/ac3b53e9da8c4044ba6c322589b80e45