A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models

By K. Sudheer, A. Gosain, K. Ramasastri. 2002

433 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c8eb5d15c0625b674b141c44537f74ac8c6c78ee

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In Environmental Engineering

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Citations in the corpus

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes (2013, methodology): “ANN has been used in many hydrological and meteorological applications; It has been used for rainfall–runoff modelling (Akhtar et al., 2009; Chiang and Chang, 2009; Chiang et al., 2004; De Vos and Rientjes, 2005; Sudheer et al., 2002; Tokar and Johnson, 1999) for streamflow forecasting (Campolo et a”
Quantification of the predictive uncertainty of artificial neural network based river flow forecast models (2012, methodology): “It is also observed that the peak discharge is not appropriately modeled by any of the 500 ensembles, which reiterate the general concern (Sudheer et al. 2002; Srivastav et al. 2007) about the”
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer (2011, methodology): “In recent years, an artificial neural network (ANN) has been applied to solving various water resource problems including time-series forecasting (Zealand et al., 1999; Sudheer et al., 2002; Cigizoglu, 2003; Almasri and Kaluarachchi, 2005; Yoon et al., 2007).”
A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method (2017, methodology): “Artificial Neural Network (ANN) has been successfully used in various fields such as hydrology and water resources (Sudheer et al. 2002; Yoon et al. 2007).”
Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones (2005, background): “…context, recent experiments have reported that artificial neural networks (ANNs) may offer a promising alternative for rainfall–runoff modelling (Sudheer et al. , 2002; Wilby et al ., 2003; Solomatine & Dulal, 2003), streamflow prediction (Raman & Sunilkumar, 1995; Zealand et al. , 1999;…”
Methods to improve neural network performance in daily flows prediction (2009, methodology): “Sudheer et al. (2002) suggested that the statistical approach 352 depending on cross-, auto- and partial-auto-correlation of the observed data is a good 353 alternative to the trial-and-error method in identifying model inputs.”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “Based on PACFs in the input selection process, a limited, yet more appropriate set of predictor variables were utilized that comprised the EDI of the current month and the lagged EDI of the past 1 month for a 1 month lead time EDI forecast.”
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”
Using support vector machines for long-term discharge prediction (2006, methodology): “Sudheer et al. (2002) suggested a statistical procedure for identifying the appropriate input vector for a model.”
Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, methodology): “455 Sudheer et al. (2002) suggested that the issue of stationarity should be considered in the ANN 456 development because the ANN cannot account for trends and heteroscedasticity in the data.”
A neuro-fuzzy computing technique for modeling hydrological time series (2004, methodology): “However, a model with a minimum RMSE may not be sufficient to eliminate the uncertainty in model structure choice (Sudheer et al., 2002b).”