A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

By K. Luk, J. Ball, Ashish Sharma. 2000

328 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/2ffc8d745e150783e064e4e33c12bc1bcb4b417f

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In Artificial Intelligence

In Electrical and Electronic Engineering

In Environmental Engineering

Citations in the corpus

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes (2013, methodology): “Through using this method, the network stops the training when the error over the validation set starts to increase while the error over training set is still decreasing; In this way the network avoids over fitting (Luk et al., 2000; Sarle, 1995).”
Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia (2015, result): “In fact, recent studies that compared rainfall predictions using ML models with the physical models demonstrated dramatic improvements in the prediction capability of the formermodels (Abbot andMarohasy, 2012, 2014; Luk et al., 2000;Mekanik et al., 2013;Nasseri et al., 2008).”
Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals (2016, methodology): “Through using this method, the network stops the training when the error over the validation set starts to increase while the error over training set is still decreasing (Luk et al. 2000).”
Modelling level change in lakes using neuro-fuzzy and artificial neural networks (2009, unknown): “(French et al., 1992; Grimes et al., 2003; Luk et al., 2000; Baratti et al., 2003; Chang and Chen, 2001; Chang et al., 2004; Dawson and Wilby, 1998; Hsu et al., 1995; Karunanithi et al., 1994; Xu and Li, 2002; Zhu and Fujita, 1994; Lallahem and Mania, 2003; Yang et al., 1997; Hasebe and Nagayama, 20”
Adaptive neuro-fuzzy inference system for prediction of water level in reservoir (2006, methodology): “In the hydrological forecasting context, ANNs have also proven to be an efficient alternative to traditional methods for rainfall forecasting [13,15,26], streamflow forecasting [2–4,6,10,18,22,36,39], groundwater modeling [24,37], and reservoir operation [17,19,30].”
Numerical assessment of metamodelling strategies in computationally intensive optimization (2012, methodology): “As a result, ANNs have been widely and successfully applied in relating or approximating different error-prone variables existing in the water resources problems including: meteorological variable forecasting (e.g., Karamouz et al., 2008; Luk et al., 2000), rainfall-runoff modelling (e.”
Quantification of the predictive uncertainty of artificial neural network based river flow forecast models (2012, methodology): “However, the relationship between the variables is not clearly known a priori, and hence often an analytical technique, such as cross-correlation, is employed (e.g. Sajikumar and Thandaveswara 1999; Luk et al. 2000; Silverman and Dracup 2000; Sudheer et al. 2002).”
Input determination for neural network models in water resources applications. Part 1—background and methodology (2005, methodology): “Methods based on linear cross-correlation: When the relationship to be modelled is not well understood, then an analytical technique, such as cross-correlation, is often employed (e.g. Golob et al., 1998; Huang and Foo, 2002; Luk et al., 2000; Sajikumar and Thandaveswara, 1999; Silverman and Dracup,”
Drought forecasting using artificial neural networks and time series of drought indices (2007, methodology): “Hall (1999) applied ANN for rainfall forecasting in Texas, Kuligowski and Barros (1998) – to predict 6-h rainfalls in two drainage basins in Pennsylvania, Luk et al. (2000) – for short-term rainfall forecasting within a flood warning system and Ramirez et al. (2005) – for daily rainfall forecasting.”