Detecting Water Depth from Remotely Sensed Imagery Based on ELM and GA-ELM

Zheng, Guo; Hua, Weihua; Qiu, Zhonghang; Gong, Zimei

doi:10.1007/s12524-020-01270-w

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shi et al [91] used GA to perform parameter optimization of the BP model estimating the chlorophyll content of winter wheat, in which the modeling accuracy of GA_BP was higher than that of the BP model. Zheng et al [92] used the GA to optimize ELM for shallow sea bathymetry inversion, confirming that the accuracy of the GA_ELM model was higher than that of the ordinary ELM model. Our study also found that the simulation accuracy of the models optimized by GA was generally higher.…”

Section: Optimizing Rf Bp and Kelm With Gamentioning

confidence: 95%

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Miao,

Chen,

Guo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Anthocyanin can improve the stress tolerance and disease resistance of winter wheat to a certain extent, so timely and accurate monitoring of anthocyanin content is crucial for the growth and development of winter wheat. This study measured the ground-based hyperspectral reflectance and the corresponding anthocyanin concentration at four key growth stages—booting, heading, flowering, and filling—to explore the spectral detection of anthocyanin in winter wheat leaves. Firstly, the first-order differential spectra (FDS) are obtained by processing based on the original spectra (OS). Then, sensitive bands (SBS), the five vegetation indices for optimal two-band combinations (VIo2), and the five vegetation indices for optimal three-band combinations (VIo3) were selected from OS and FDS by band screening methods. Finally, modeling methods such as RF, BP, and KELM, as well as models optimized by genetic algorithm (GA), were used to estimate anthocyanin content at different growth stages. The results showed that (1) among all the models, the GA_RF had incredible performance, VIo3 was the superior parameter for estimating anthocyanin values, and the model GA_RF of FDS data based on VIo3 for the filling stage (Rv2 = 0.950, RMSEv = 0.005, RPDv = 4.575) provided the best estimation of anthocyanin. (2) the first-order differential processing could highlight the degree of response of SBS, VIo2, and VIo3 to the anthocyanin values. The model performances of the FDS were better than that of OS on the whole, and the Rv2 of the optimal models of FDS were all greater than 0.89. (3) GA had optimizing effects on the RF, BP, and KELM, and overall, the GA models improved the R2 by 0.00%-18.93% compared to the original models. These results will provide scientific support for the use of hyperspectral techniques to monitor anthocyanin in the future.

show abstract

Section: Optimizing Rf Bp and Kelm With Gamentioning

confidence: 95%

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Miao,

Chen,

Guo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The extreme learning machine is an algorithm designed for a feed-forward neural network based on the traditional neural network [45]. However, the connection weights from the input layer to the hidden layer and the threshold value of the hidden layer are given manually or randomly, so only the connection weights between the hidden layer and the output value need to be calculated.…”

Section: A Extreme Learning Machine ( Elm )mentioning

confidence: 99%

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Yao

Xiao

et al. 2022

IEEE Access

View full text Add to dashboard Cite

The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.

show abstract

“…Extreme Learning Machine algorithm (ELM) is a commonly used tool for predicting carbon price, which can be proved in studies (Taormina and Chau, 2015), (Yadav et al, 2016), (Adnan et al, 2019) and (Xu et al, 2020). However, the randomness of the input weight matrix initialized by ELM and hidden layer bias will reduce the accuracy of the model (Zheng et al, 2020). It is a good choice to use the global searching ability of the genetic algorithm to optimize the parameters in the model.…”

Section: Introductionmentioning

confidence: 99%

Optimized Extreme Learning Machine (ELM) Based on Genetic Algorithm (GA) To Predict Carbon Price Under The Influence of Multiple Factors

Sun

Ren

2021

Preprint

View full text Add to dashboard Cite

The promotion of carbon market can accelerate the pace of low-carbon transformation of China's economic structure and achieve more efficient carbon emission reduction. Accurate carbon price prediction is conducive to improving the risk management of carbon market and the decision-making of investors, but it also brings great challenges to relevant industry practitioners and the government. In this paper, a new hybrid model is proposed, which combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and genetic algorithm (GA) optimized extreme learning machine (ELM). The application of GA-ELM in carbon price prediction is firstly studied in this paper. Eight intrinsic mode functions and one residual can be obtained by CEEMDAN, and then partial autocorrelation (PACF) is used to determine the partial correlation between each sequence and its lag data, and they were taken as internal factors affecting the prediction. At the same time, energy, economic and social factors are selected as the external factors affecting the prediction, and the carbon price prediction is realized through internal and external factors. It has been proved that the model successfully overcomes the challenge of carbon price prediction based on multiple influencing factors. The hybrid model shows superiority in Beijing, Shanghai and Guangdong. The results show that the prediction performance of the proposed model is the best among the 15 models, and the prediction accuracy will be improved due to the decomposition of the carbon price. Besides, the CEEMDAN-GA-ELM model better overcomes the challenge of carbon price prediction with multiple influencing factors. This model provides a novel and effective tool for the government and enterprises to predict the carbon price.

show abstract

Detecting Water Depth from Remotely Sensed Imagery Based on ELM and GA-ELM

Cited by 6 publications

References 17 publications

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models

Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Optimized Extreme Learning Machine (ELM) Based on Genetic Algorithm (GA) To Predict Carbon Price Under The Influence of Multiple Factors

Contact Info

Product

Resources

About