Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting

Qu, Jihong; Ren, Kun; Shi, Xiaoyu

doi:10.1007/s11269-021-02770-1

Cited by 28 publications

(4 citation statements)

References 50 publications

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“…In this study, VIs with heritability greater than 0.5 were selected across the growth stages, which means that all the base learners and their combinations were evaluated at maximum input data accuracy and repeatability. Previously, feature selection algorithms such as recursive feature elimination (RFE) [37] and Boruta [67] have been applied to prediction analysis. Different from RFE and Boruta, the feature selection based on heritability can be performed without knowing the predictor variables in breeding work.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Fei

Hassan

et al. 2021

Remote Sensing

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Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield between for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

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Section: Discussionmentioning

confidence: 99%

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Fei

Hassan

et al. 2021

Remote Sensing

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“…A similar approach is used in [44], which found superior results by a hybrid EEMD-Boruta-ELM model when forecasting soil moisture. The importance of input selection by BRF for data-driven streamflow forecasting is also demonstrated in [45].…”

Section: Introductionmentioning

confidence: 99%

An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia

Raj

Brown

2021

Remote Sensing

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Using advanced deep learning (DL) algorithms for forecasting significant wave height of coastal sea waves over a relatively short period can generate important information on its impact and behaviour. This is vital for prior planning and decision making for events such as search and rescue and wave surges along the coastal environment. Short-term 24 h forecasting could provide adequate time for relevant groups to take precautionary action. This study uses features of ocean waves such as zero up crossing wave period (Tz), peak energy wave period (Tp), sea surface temperature (SST) and significant lags for significant wave height (Hs) forecasting. The dataset was collected from 2014 to 2019 at 30 min intervals along the coastal regions of major cities in Queensland, Australia. The novelty of this study is the development and application of a highly accurate hybrid Boruta random forest (BRF)–ensemble empirical mode decomposition (EEMD)–bidirectional long short-term memory (BiLSTM) algorithm to predict significant wave height (Hs). The EEMD–BiLSTM model outperforms all other models with a higher Pearson’s correlation (R) value of 0.9961 (BiLSTM—0.991, EEMD-support vector regression (SVR)—0.9852, SVR—0.9801) and comparatively lower relative mean square error (RMSE) of 0.0214 (BiLSTM—0.0248, EEMD-SVR—0.043, SVR—0.0507) for Cairns and similarly a higher Pearson’s correlation (R) value of 0.9965 (BiLSTM—0.9903, EEMD–SVR—0.9953, SVR—0.9935) and comparatively lower RMSE of 0.0413 (BiLSTM—0.075, EEMD-SVR—0.0481, SVR—0.057) for Gold Coast.

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“…Although RELM solves the overfitting problem compared to ELM, RELM also has some problems, such as the selection of the appropriate regularization factor is random and time-consuming. Several scholars have also studied the improvement of RELM: to automatically select a satisfactory regularization factor, Y. Zhang et al [12] proposed an adaptive RELM with a function instead of a regularization factor; C. Gautam et al [13] proposed an ELM with a regularization kernel, used a single-class ELM classifier based on the regularization kernel to detect outliers, and extended it to adaptive online learning, whose experimental results show that the classifier has faster learning capability and is more suitable for real-time anomaly detection; reference [14] proposed a new binary grey wolf optimization-regularized extreme learning machine wrapper; reference [15] used adaptive whale optimization algorithm (AWOA) to determine the input weights and hidden layer deviations of ELM. ; Kumar et al [16]and L. Zhi et al [17] proposed biogeography-based extreme learning machine (BBO-ELM)model and a GAPSO-Enhanced ELM method respectively.…”

Section: Introductionmentioning

confidence: 99%

An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine

Tang

2021

IEEE Access

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Extreme learning machine (ELM) is a novel single-hidden layer feedforward neural network to obtain fast learning speed by randomly initializing weights and deviations. Due to its extremely fast learning speed, it has been widely used in training of massive data in recent years. In order to adapt to the real network environment, based on the ELM, we propose an improved particle swarm optimized online regularized extreme learning machine (IPSO-IRELM) intrusion detection algorithm model. First, the model replaces the traditional batch learning with sequential learning by dynamically adapting the new data obtained in the training network instead of training all collected samples in an offline manner; second, we improve the particle swarm optimization algorithm and compare it with typical improved algorithms to prove its effectiveness; finally, to solve the random initialization problem of IRELM, we use IPSO to optimize the initial weights and deviations of IRELM to improve the classification ability of IRELM. The experimental results show that IPSO-IRELM algorithm has better generalization ability, which not only improves the accuracy of intrusion detection, but also has certain recognition ability for minority class samples.

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Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting

Cited by 28 publications

References 50 publications

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

An EEMD-BiLSTM Algorithm Integrated with Boruta Random Forest Optimiser for Significant Wave Height Forecasting along Coastal Areas of Queensland, Australia

An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine

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