Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting

Madhiarasan, M.; Deepa, S. N.

doi:10.1007/s10462-016-9506-6

Cited by 85 publications

(50 citation statements)

References 27 publications

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“…Here, we used the one-hidden-layer network because it was demonstrated to be powerful enough to approximate any measurable function to any desired degree of accuracy [59]. Many studies have been dedicated to the investigation of the optimal number of neurons in the hidden layer, and several empirical equations have been proposed [60][61][62]. In this study, the number of neurons in the hidden layer is determined by the following equation [62]:…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

Mao

Meng

et al. 2019

Applied Sciences

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Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random forest (RF) and gradient boosting regression tree (GBRT), have been used in the retrieval of cotton LAI with Sentinel-2 spectral bands. The performances of the five machine learning models are compared for better applications of MLRAs in remote sensing, since challenging problems remain in the selection of MLRAs for crop LAI retrieval, as well as the decision as to the optimal number for the training sample size and spectral bands to different MLRAs. A comprehensive evaluation was employed with respect to model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. We conducted the comparison of five MLRAs in an agricultural area of Northwest China over three cotton seasons with the corresponding field campaigns for modeling and validation. Results show that the GBRT model outperforms the other models with respect to model accuracy in average ( R 2 ¯ = 0.854, R M S E ¯ = 0.674 and M A E ¯ = 0.456). SVR achieves the best performance in computational efficiency, which means it is fast to train, and to validate that it has great potentials to deliver near-real-time operational products for crop management. As for sensitivity to training sample size, GBRT behaves as the most robust model, and provides the best model accuracy on the average among the variations of training sample size, compared with other models ( R 2 ¯ = 0.884, R M S E ¯ = 0.615 and M A E ¯ = 0.452). Spectral bands sensitivity analysis with dCor (distance correlation), combined with the backward elimination approach, indicates that SVR, GPR and RF provide relatively robust performance to the spectral bands, while ANN outperforms the other models in terms of model accuracy on the average among the reduction of spectral bands ( R 2 ¯ = 0.881, R M S E ¯ = 0.625 and M A E ¯ = 0.480). A comprehensive evaluation indicates that GBRT is an appealing alternative for cotton LAI retrieval, except for its computational efficiency. Despite the different performance of the ML models, all models exhibited considerable potential for cotton LAI retrieval, which could offer accurate crop parameters information timely and accurately for crop fields management and agricultural production decisions.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

Mao

Meng

et al. 2019

Applied Sciences

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“…When the layer is set as two, the classification accuracy is low. Therefore, to balance the accuracy and time cost [49,50], four hidden layers are selected in this experiment. The number of neurons for each hidden layer is set according to the experience of multiple trials, and the principle is still balancing time cost and accuracy.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

Jian

Long

et al. 2018

Remote Sensing

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Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25-100%, and the kappa coefficients of the MLP range from 0.965-1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1-7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet.

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“…These methods include physical methods (Landberg 1999;Negnevitsky and Poter 2006;Lange and Focken 2009), statistical methods (Ma et al 2009), and a combination of the two (Zhang et al 2014). Currently, artificial neural networks (ANNs) are widely used in wind speed forecasting, including multi-layer perceptron (Madhiarasan and Deepa 2017), radial basis function (Zhang et al 2018), recurrent neural networks (Shao, Deng, and Jiang 2018;Shi, Liang, and Dinavahi 2018), amongst others. Early studies also showed that artificial intelligence (AI) methods appear to be more accurate compared to traditional statistical models.…”

Section: Introductionmentioning

confidence: 99%

Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm

Zhang

Yang

Guo

et al. 2019

Atmospheric and Oceanic Science Letters

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Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and comparedwith each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition (WD), the Cuckoo search (CS) optimization algorithm, and a wavelet neural network (WNN). They are referred to as CS-WD-ANN (artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.

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Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting

Cited by 85 publications

References 27 publications

Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm

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