Training Artificial Neural Networks to Perform Rainfall Disaggregation

Burian, Steven J.; Durrans, Stéphanie; Nix, Stephan J.; Pitt, Robert

doi:10.1061/(asce)1084-0699(2001)6:1(43)

Cited by 47 publications

(18 citation statements)

References 17 publications

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“…However, the error on a different data set, which was not used for the purpose of training, called as validation error, decreases monotonically to a minimum but then starts increases, even as the training error continues to decrease (Burian et al, 2001;Hassoun 1999). When ANN is used to train noisy data, it would initially learn the actual pattern, therefore the validation error decreases initially along with the training error.…”

Section: Termination Criteriamentioning

confidence: 99%

“…After learning the actual pattern, the ANN may try to learn the noise also, thus the validation error may increase even if the training error continue to decrease. In general practice, the validation error is carefully monitored during the training phase, and the training process is terminated just before the increase in validation error (Burian et al, 2001;Hassoun 1999). However, in this study, the training is continued for some more iteration after reaching minimum of the validation error to check that the obtained solution is not a local optimal solution.…”

Section: Termination Criteriamentioning

confidence: 99%

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Performance of an Artificial Neural Network model for simulating saltwater intrusion process in coastal aquifers when training with noisy data

2009

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This paper evaluates the performance of an Artificial Neural Networks (ANN) model for approximating density depended saltwater intrusion process in coastal aquifer when the ANN model is trained with noisy training data. The data required for training, testing and validation of the ANN model are generated using a numerical simulation model. The simulated data, consisting of corresponding sets of input and output patterns are used for training a multilayer perception using back-propagation algorithm. The trained ANN predicts the concentration at specified observation locations at different time steps. The performance of the ANN model is evaluated using an illustrative study area. These evaluation results show the efficient predicting capabilities of an ANN model when trained with noisy data. A comparative study is also carried out for finding the better transfer function of the artificial neuron and better training algorithms available in Matlab for training the ANN model.

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Section: Termination Criteriamentioning

confidence: 99%

Section: Termination Criteriamentioning

confidence: 99%

Performance of an Artificial Neural Network model for simulating saltwater intrusion process in coastal aquifers when training with noisy data

2009

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“…In recent years, artificial neural networks (ANNs), under nonparametric category, have found their ways to solve many problems related to rainfall such as rainfall forecasting [13][14][15][16][17][18][19][20], rainfall-runoff model [21][22][23], rainfall estimation by radars [1,24,25] and satellites [26][27][28][29][30][31], and temporal and spatial rainfall disaggregation [32,33]. e advantage of an ANN approach is that it can be used to develop a functional relationship, including a nonlinear relationship, amongst the various parameters of the process under study even in the absence of full understanding of its mathematical model [34].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Misra

Deshamukhya

Sharma³

et al. 2018

Advances in Meteorology

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In the present work, daily rainfall is simulated over the core monsoon region of India by using a feedforward multilayer perceptron (MLP) model. Daily rainfall is found to be optimally dependent on four concurrent meteorological parameters, namely, geopotential height, specific humidity, zonal, and meridional wind at 1000 mb, 925 mb, 850 mb, and 700 mb pressure levels during 00, 06, 12, and 18 Greenwich Mean Time (GMT). e architecture of the optimized feedforward MLP model consists of 64 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. e results from the model are compared with the 3B42 (version 7) rainfall product. In terms of root mean square error (rmse) and correlation coefficient (cc), the model is performing better compared to the satellite-derived 3B42 rainfall product, whereas in terms of bias, the performance of the 3B42 product is better compared to the model. e weight matrices of the feedforward MLP model are estimated at a particular location (22.5°N, 82.5°E). ese weight matrices are able to simulate daily rainfall at neighbourhood locations also with reasonably good accuracy with cc in the range of 0.41 to 0.55. e performance of the model improves in case of an aerial average of daily rainfall with significantly enhanced cc (0.72). e model is able to capture monthly and intraseasonal variation of rainfall with reasonably good accuracy, with cc of 0.88 and 0.68, respectively. e simulation model has a limitation that it is not able to simulate extreme high rainfall events (>60 mm/day). Overall, the developed model is performing reasonably well. is approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. Nevertheless, the present approach can also be used for multistep prediction of rainfall.

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“…During the past decades, a variety of ANNs have been developed and applied for temporal rainfall disaggregation [1,34,35]. In this study, two ANN models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), have been applied to estimate spatial disaggregation of areal rainfall in the Wi-stream catchment.…”

Section: Introductionmentioning

confidence: 99%

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Kim

Singh

2015

Water

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Abstract:The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg-Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg-Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

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Training Artificial Neural Networks to Perform Rainfall Disaggregation

Cited by 47 publications

References 17 publications

Performance of an Artificial Neural Network model for simulating saltwater intrusion process in coastal aquifers when training with noisy data

Performance of an Artificial Neural Network model for simulating saltwater intrusion process in coastal aquifers when training with noisy data

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

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