scholarly journals Rainfall-Runoff Prediction based on Artificial Neural Network: A Case Study Priyadarshini Watershed

2019 ◽  
Vol 8 (05) ◽  
pp. 1328-1334
Author(s):  
S.K. Kothe ◽  
B.L. Ayare ◽  
H.N. Bhange ◽  
S.T. Patil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Artificial Neural

Artificial Neural Network Model for Rainfall-Runoff -A Case Study

2016 ◽  
Vol 9 (3) ◽  
pp. 263-272 ◽  
Author(s):  
P.Sundara Kumar ◽  
T. V. Praveen ◽  
M. Anjanaya Prasad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rainfall Runoff ◽  
Artificial Neural

scholarly journals Investigation the Ability of Artificial Neural Network in Simulation of Rainfall-Runoff Process under the Climate Change Conditions (Case Study: Pashakola Babol Dam Basin)

2018 ◽  
Vol 8 (16) ◽  
pp. 53-64
Author(s):  
معصومه خادمی ◽  
رامین فضل‌ اولی ◽  
علیرضا عمادی ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Artificial Neural

scholarly journals Prediction of annual runoff using Artificial Neural Network and Regression approaches

MAUSAM ◽  
2021 ◽  
Vol 62 (1) ◽  
Author(s):  
N. VIVEKANANDAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absolute Error ◽  
Annual Runoff ◽  
Rainfall Runoff ◽  
Statistical Parameters ◽  
Runoff Prediction ◽  
Model Efficiency ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of runoff is often important for optimal design of water storage and drainage works andmanagement of extreme events like floods and droughts. Rainfall-runoff (RR) models are considered to be most effectiveand expedient tool for runoff prediction. Number of models like stochastic, conceptual, deterministic, black-box, etc. iscommonly available for RR modelling. This paper details a study involving the use of Artificial Neural Network (ANN)and Regression (REG) approaches for prediction of runoff for Betwa and Chambal regions. Model performanceindicators such as model efficiency, correlation coefficient, root mean square error and root mean absolute error are usedto evaluate the performance of ANN and REG for runoff prediction. Statistical parameters are employed to find theaccuracy in prediction by ANN and REG for the data under study. The paper presents that ANN approach is found to besuitable for prediction of runoff for Betwa and Chambal regions.

A non-linear rainfall–runoff model using an artificial neural network

1999 ◽  
Vol 216 (1-2) ◽  
pp. 32-55 ◽  
Author(s):  
N. Sajikumar ◽  
B.S. Thandaveswara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Non Linear ◽  
Artificial Neural ◽  
Rainfall Runoff Model ◽  
Runoff Model
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