Modeling of the daily rainfall-runoff relationship with artificial neural network

By M. P. Rajurkar, U. C. Kothyari, U. C. Chaube. 2004

300 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/44542310b131d76a0421f760bb9d4eb2087bca14

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions (2010, background): “…Flow Sudheer and Jain (2004) Narmada River, India Flow Shu and Burn (2004) 404 Catchments in UK Flow Riad et al. (2004) Ourika River Morocco Flow Rajurkar et al. (2004) Krishna & Narmada-India; Bird Creek-USA; Brosna-Ireland; GarrapatasColumbia; Kizu-Japan; Pampanga-Phillipines Flow Pan and…”
Forecasting daily streamflow using hybrid ANN models (2006, methodology): “…to increase the likelihood of obtaining nearoptimum set of parameters is to train the ANN with different independent initial weights, then the model builders choose the best ANN model according to the training or validation performance among many competitive ANN models (e.g. Rajurkar et al., 2004).”
A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, methodology): “Machine learning (ML) methods such as artificial neural network (ANN) (Rajurkar et al. 2004), fuzzy systems (FS) (Xiong et al.”
HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts (2007, unknown): “‘‘error in volume’’ (Rajurkar et al., 2004); ‘‘error of total runoff volume’’ (EV; Lin and Chen, 2004); ‘‘percent bias’’ (PBIAS; Yapo et al.”
Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “Among the EPS methods, the ensembles of ANN, MLP, SVM, and RF showed promising results.”
Pareto genetic design of group method of data handling type neural network for prediction discharge coefficient in rectangular side orifices (2015, methodology): “Therefore, models presented herein had to be evaluated using other indexes such as average absolute relative error (AARE) and threshold statistics [40–43].”
Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test (2018, methodology): “multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), co-active neuro-fuzzy inference system (CANFIS), generalized regression neural network (GRNN), radial basis neural network (RBNN), selforganizing mapping neural network (SOMNN), genetic algorithm (GA), wave”