Kohonen Neural Networks for Rainfall-Runoff Modeling: Case Study of Piancó River Basin

Farias, Camilo Allyson Simões de; Santos, Celso Augusto Guimarães; Lourenço, Artur M. G.; Carneiro, Tatiane C.

doi:10.4090/juee.2013.v7n1.176182

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…After the training, SOM can be used as a forecasting tool 31 . The input vector should be considered in the absence of the variable to be predicted, following these steps: Calculate the DI i between the input vectors and weights connected to the output neuron disregarding element j to be predicted.…”

Section: Methodsmentioning

confidence: 99%

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Methodology for integration of wind resource forecasts based on artificial neural networks

Carneiro

Lima²,

Carvalho

et al. 2022

Intl J of Energy Research

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Summary An adaptation of the portfolio theory (PT) is proposed in this article, denoted as PrevPT, “Previsão” (in Portuguese) by PT, to integrate the three artificial neural networks, namely multilayer perceptron (MLP) backpropagation, radial basis function (RBF), and self‐organizing map (SOM), based forecasting techniques, aiming to analyze the impact of wind speed forecasting errors and achieve more accurate results. In its first use, the PT goal was to maximize a financial return, at any risk, through the diversification of securities or investments that are not positively correlated. Based on the development of PrevPT, which was used until this work only for solar forecasting, the proposed technique is applied in this paper to integrate and improve the results of individual wind forecasts. Four‐year wind speed data (January 2007 to December 2010) from two different locations (Algeciras, Spain and Petrolina, Brazil) were used. Our methodology develops a topology that integrates the forecasts obtained by MLP, RBF, and SOM aiming to obtain smaller forecast errors. By diversifying the forecasted asset, when one of the assets has negative prediction errors, another compensates for them and, thus, the total or partial cancellation of the errors occurs. PrevPT obtains a mean absolute percentage error of 1.13% for Spain and 2.35% for Brazil. PrevPT surpassed the results obtained by the three techniques applied individually in the two locations. The main innovations of the methodology are the significant reduction of errors and optimization of resource planning, and the beneficial features compared to other predictor integration techniques.

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Section: Methodsmentioning

confidence: 99%

“…29 After the training, SOM can be used as a forecasting tool. 31 The input vector should be considered in the absence of the variable to be predicted, following these steps:…”

Section: Self Organizing Mapmentioning

confidence: 99%

Methodology for integration of wind resource forecasts based on artificial neural networks

Carneiro

Lima²,

Carvalho

et al. 2022

Intl J of Energy Research

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“…Our methodology is divided into two parts. Initially we use two ANN to forecast electricity generation (G) to 2050, considering, as a priority, the possibility of processing a limited number of input data: cascade forward back propagation (CFBP) 68 and Kohonen neural networks (KNN) 69 . In a second phase, we multiply the generation value by the WCEP of different electricity generation scenarios, exploring the implications of different sets of plants, installed and with potential for installation.…”

Section: Proposed Methodology To Evaluate W‐en To 2050mentioning

confidence: 99%

“…A two‐dimensional output layer composed of 25 neurons is chosen. After the training, vectors representative of the series behavior are used to estimate the electricity generation until 2050, adapting the methodology proposed in Reference 69. We use C and GDP variations and their influence to obtain the future generation, following the steps:…”

Section: Proposed Methodology To Evaluate W‐en To 2050mentioning

confidence: 99%

A new methodology to estimate future water‐energy nexus based on artificial neural networks

Carvalho

Carneiro

2021

Int J Energy Res

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Summary We propose a new methodology to evaluate future electricity generation scenarios aiming to explore implications of different sets of power plants on the water‐energy nexus (W‐EN). We use two artificial neural networks: cascade forward back propagation and Kohonen neural networks (KNN). Initially, the future electricity production of the area under investigation is determined. Sequentially, water consumption is calculated for different combinations of plants. The scenarios with the lowest annual water consumption values are based only on wind and photovoltaic (PV) plants. Despite using only renewable energy, the inclusion of concentrating solar power in a future scenario brings a significant increase in water consumption, challenging the sustainability of this matrix. A conservative scenario (50% of electricity production by thermal power plants and 50% by renewable energy) shows the highest annual water consumption, also a barrier to sustainability.

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“…Other application is the work of Adeloye et al (2011), who proposed an SOM estimator for reference crop evapotranspiration and obtained results superior to those from recommend empirical methods. Farias et al (2013) developed an SOM-based rainfall-runoff model and verified it was reliable for estimating streamflows in Piancó River, Brazil.…”

Section: Introductionmentioning

confidence: 93%

Runoff-erosion modeling at micro-watershed scale: a comparison of self-organizing maps structures

Farias

Bezerra

Filho

2015

GEOENVIRON DISASTERS

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Background: In the last decades, everal runoff-erosion models have been proposed to estimate soil erosion, which may lead to loss of fertile land and increase sedimentation and pollution in water bodies. Physically-based erosion models are usually used for such purpose, but a major problem concerning their use is the difficulty to directly measure parameters in the field. This problem can be overcome by exploring empirical models, such as so-called Self-Organizing Maps (SOM). An SOM is a type of Artificial Neural Network (ANN) based on a competitive learning approach for clustering and modeling a variety of databases. Since studies on soil erosion modeling based on SOM are very incipient, we compared some structures of SOM with the purpose of estimating sediment yield based on runoff and climatological data at the micro-watershed scale. The case study was a micro-watershed within the Sumé Experimental Basin, which is located in a semiarid region of Brazil. Different from the conventional ANN, SOM-based models represent a multidimensional data set by means of a bidimensional matrix of features, which may be applied for analysis and estimation purposes. In order to calibrate and validate the proposed SOM structures, we used data from 117 rainfall events that occurred between 1985 and 1991. Results: Analyses of the results indicate that all SOM structures were efficiently calibrated with NASH coefficients (Nash & Sutcliffe 1970) varying from 0.88 to 0.90. The SOM structure with 6 × 8 neurons was the most effective for estimating sediment yields when considering the validation data set (NASH = 0.73). The generated maps showed that sediment yields were directly related to runoff and rainfall intensity and inversely correlated to average vegetation heights. The dry period length did not seem to influence the production of sediments. Conclusions: SOM were shown to be very practical and meant to be applied to specific locations. This type of methodology also demands long term data and dynamic recalibration with up-to-date information in order to account for changes in the watershed.

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Kohonen Neural Networks for Rainfall-Runoff Modeling: Case Study of Piancó River Basin

Cited by 13 publications

References 11 publications

Methodology for integration of wind resource forecasts based on artificial neural networks

Methodology for integration of wind resource forecasts based on artificial neural networks

A new methodology to estimate future water‐energy nexus based on artificial neural networks

Runoff-erosion modeling at micro-watershed scale: a comparison of self-organizing maps structures

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