Extreme Learning Machine Model Research Articles

Long-term safety evaluation of soft rock tunnel structure based on knowledge decision-making and data-driven models

Tunnel structure are often subjected to continuous deformation caused by the creep effect of soft rock, leading to posing significant long-term operational risks. This paper focuses on ensuring the safe operation of a typical soft rock tunnel in the complex and dangerous mountainous area of western China. On the basis of studying the structural mechanic characteristics of the tunnel under the influence of soft rock creep, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model based on Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to determine subjective and objective weights, and game theory (GT) to obtain comprehensive weights was established. Additionally, the differential evolution-grey wolf optimization (DE-GWO) improved extreme learning machine (ELM) model was also constructed. By comparing the safety evaluation results of the two models on typical monitoring sections of soft rock tunnel structure, it is demonstrated that the proposed knowledge-based decision-making and data-driven models effectively and accurately evaluated the long-term safety of typical monitoring sections. Ulteriorly, to address the issue of structure safety evaluation caused by the inability to comprehensively monitor the entire tunnel (missing monitoring data), the DE-GWO improved ELM model was further adopted, and the structure safety status of the entire tunnel was precisely deduced.

Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module

Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380–1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.

Accuracy-Improved Fault Diagnosis Method for Rolling Bearing Based on Enhanced ESGMD-CC and BA-ELM Model

The current methods for early fault diagnosis of rolling bearing have some flaws, such as poor fault feature information and insufficient fault feature extraction capability, which makes it challenging to guarantee fault diagnosis accuracy. In order to increase the accuracy of fault diagnosis, it proposes a new fault diagnosis method based on enhanced Symplectic geometry mode decomposition with cosine difference factor and calculus operator (ESGMD-CC) and bat algorithm (BA) optimized extreme learning machine (ELM). The vibration signal is first decomposed into a number of Symplectic geometry components (SGCs) by SGMD. The number of iterations is reduced by the cosine difference factor, which also successfully separates the noise components from the effective components. The calculus operator is adopted to strengthen the weak fault features, making it simple to extract. The fault feature vectors are calculated by the power spectrum entropy-weighted singular values. Finally, the ELM model optimized by BA iteratively is performed as the final classifier for fault classification. The simulation and experiments demonstrate that the proposed method has a better degree of fault diagnostic accuracy and is effective at extracting the rich fault information from vibration signals.

Bayesian hybrid-kernel machine-learning-assisted sensitivity analysis and sensitivity-relevant inverse modeling for groundwater DNAPL contamination

Accurate source characterization and transport parameter estimation is important when seeking to predict the spatiotemporal distribution of dense non-aqueous phase liquid (DNAPL) contaminants in groundwater. However, this is a complex multimodal search problem prone to equifinality and premature convergence, which leads to considerable error. To address this, a sensitivity-relevant dynamic swarm intelligence (SRD-SI) algorithm embedded in a homotopy-variation mechanism is proposed in the present study to rationally balance the inversion processes of sensitivity-varied source characteristics and DNAPL transport parameters. In this approach, global optima are progressively approached in conjunction with the homotopy variation of the search space. Furthermore, to avoid computationally expensive numerical model repetition during the sensitivity analysis and inverse iterations, a Bayesian-based optimization framework that combines multiple kernel functions in a kernel extreme learning machine (KELM) model is designed considering the complex site conditions and statistical characteristics of the input variables, thus creating the Bayesian hybrid KELM (BHK-ELM) model for the reliable surrogate modeling of numerical DNAPL-transport simulations. The results show that the BHK-ELM model recognizes and effectively reconstructs the complex input − output mapping of the numerical model by increasing the determination coefficient R2 to 0.9988 while improving the computational efficiency approximately 4500-fold. Because source characteristics and boundary conditions are far more sensitive than transport parameters to the contaminant distribution, conventional inverse modeling methods struggle to accurately identify these transport parameters. In contrast, the proposed inverse modeling system combining sensitivity analysis, swarm intelligence, and homotopy variation is more stable and provides significantly more accurate estimations for all unknown variables. Compared with the traditional SI algorithm, the homotopy-variation SRD-SI reduced the maximum inversion relative error from 46.22 % to 9.53 %, while the mean inversion relative error was reduced from 11.09 % to 3.90 %.

Ensemble and Pre-Training Approach for Echo State Network and Extreme Learning Machine Models.

The echo state network (ESN) is a recurrent neural network that has yielded state-of-the-art results in many areas owing to its rapid learning ability and the fact that the weights of input neurons and hidden neurons are fixed throughout the learning process. However, the setting procedure for initializing the ESN's recurrent structure may lead to difficulties in designing a sound reservoir that matches a specific task. This paper proposes an improved pre-training method to adjust the model's parameters and topology to obtain an adaptive reservoir for a given application. Two strategies, namely global random selection and ensemble training, are introduced to pre-train the randomly initialized ESN model. Specifically, particle swarm optimization is applied to optimize chosen fixed and global weight values within the network, and the reliability and stability of the pre-trained model are enhanced by employing the ensemble training strategy. In addition, we test the feasibility of the model for time series prediction on six benchmarks and two real-life datasets. The experimental results show a clear enhancement in the ESN learning results. Furthermore, the proposed global random selection and ensemble training strategies are also applied to pre-train the extreme learning machine (ELM), which has a similar training process to the ESN model. Numerical experiments are subsequently carried out on the above-mentioned eight datasets. The experimental findings consistently show that the performance of the proposed pre-trained ELM model is also improved significantly. The suggested two strategies can thus enhance the ESN and ELM models' prediction accuracy and adaptability.

An Inversion Study of Reservoir Colluvial Landslide Permeability Coefficient by Combining Physical Model and Data-Driven Models

The saturated permeability coefficient (ks) is a key parameter for evaluating the seepage and stability of reservoir colluvial landslides. However, ks values obtained from traditional experimental methods are often characterized by large variations and low representativeness. As a result, there are significant deviations from actual observations when used in seepage field calculations for reservoir landslide analysis. This study proposes an intelligent inversion method that combines a physical model and a data-driven model for reservoir landslide ks based on actual groundwater level (GWL) monitoring data. This method combines Latin Hypercube Sampling (LHS), unsaturated flow finite element (FE) analysis, particle swarm optimization algorithm (PSO), and kernel extreme learning machine model (KELM). Taking the Hongyanzi landslide in Sichuan Province, China, as the research object, the GWL of the landslide under different ks was first obtained by LHS and transient seepage FE analysis. Then, a nonlinear functional relationship between ks and the landslide GWL was fitted based on the PSO-KELM model. Finally, the optimal landslide ks was obtained by minimizing the root-mean-squared error between the predicted and actual GWL using the PSO. A global sensitivity analysis was also conducted on the ks of different rock and soil layers to reveal their control rules on the calculation of landslide GWL. The research results demonstrate the feasibility of the proposed method and provide valuable information for similar landslides in practice.

Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram

Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.

A food quality detection method based on electronic nose technology

Food quality detection is of great importance for human health and industrial production. Currently, the common detection methods are difficult to achieve the need for fast, accurate, and non-destructive detection. In this work, an electronic nose (E-nose) detection method based on the combination of convolutional neural network combined with wavelet scattering network (CNN-WSN) and improved seahorse optimizes kernel extreme learning machine (ISHO-KELM) is proposed for identifying the quality level of a variety of food products. In the feature extraction part, the abstract features of CNN are fused with the scattering features of WSN, and the obtained CNN-WSN fusion features can characterize the original information of the food quality effectively. In the classifier design and decision-making section, chaotic mapping is used to initialize the population in the seahorse optimisation algorithm (SHO), avoiding the problem that SHO may fall into local optimal solutions. The kernel parameters and regularisation coefficients of the KELM model were then optimized by improving the locomotion, predation, and reproduction behaviors of the hippocampal populations, which solved the problem of the difficult selection of the key parameters in the model, and thus improved the accuracy and generalization of the overall model. To validate the effectiveness of the proposed food quality detection model, the E-nose system was first built and milk quality data were collected independently, and then tested on two publicly available food quality datasets as well as a self-collected milk quality dataset, respectively. The experimental results show that the food quality detection method proposed in this work has good quality assessment effect on different datasets.

Improving extreme learning machine model using deep learning feature extraction and grey wolf optimizer: Application to image classification

The Extreme Learning Machine (ELM) is a highly efficient model for real-time network retraining due to its fast learning speed, unlike traditional machine learning methods. However, the performance of ELM can be negatively impacted by the random initialization of weights and biases. Moreover, poor input feature quality can further degrade performance, particularly with complex visual data. To overcome these issues, this paper proposes optimizing the input features as well as the initial weights and biases. We combine both Convolutional Neural Network (CNN) and Convolutional AutoEncoder (CAE) extracted features to optimize the quality of the input features. And we use our hybrid Grey Wolf Optimizer-Multi-Verse Optimizer (GWO-MVO) metaheuristic for initializing weights and biases by applying four fitness functions based on: the norm of the output weights, the error rate on the training set, and the error rate on the validation set. Our method is evaluated on image classification tasks using two benchmark datasets: CIFAR-10 and CIFAR-100. Since image quality may vary in real-world applications, we trained and tested our models on the dataset’s original and noisy versions. The results demonstrate that our method provides a robust and efficient alternative for image classification tasks, offering improved accuracy and reduced overfitting.

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.

A novel framework for lung cancer classification using lightweight convolutional neural networks and ridge extreme learning machine model with SHapley Additive exPlanations (SHAP)

This paper presents a novel approach that merges a lightweight parallel depth-wise separable convolutional neural network (LPDCNN) with a ridge regression extreme learning machine (Ridge-ELM) for precise classification of three lung cancer types alongside normal lung tissue (adenocarcinoma, large cell carcinoma, normal, and squamous cell carcinoma) using CT images. The proposed methodology combines contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur to enhance image quality, reduce noise, and improve visual clarity. The LPDCNN extracts discriminant features while minimizing computational complexity (0.53 million parameters and 9 layers). The Ridge-ELM model was developed to enhance classification performance, replacing the traditional pseudoinverse in the ELM approach. Through comprehensive evaluation against state-of-the-art models, the framework achieves remarkable average recall and accuracy values of 98.25 ± 1.031 % and 98.40 ± 0.822 %, respectively, through rigorous five-fold cross-validation for four-class classifications. In binary classifications, outstanding results are obtained with recall and accuracy values of 99.70 ± 0.671 % and 99.70 ± 0.447 %%, respectively. Notably, the framework exhibits exceptional efficiency, with a testing time of only 0.003 s. Additionally, integrating the SHAP (Shapley Additive Explanations) in the proposed framework enhances Explain-ability, providing insights into decision-making and boosting confidence in real-world lung cancer diagnoses.

Exchange rate prediction research based on LSTM-ELM hybrid model

The fluctuation of exchange rates holds paramount importance for a country's economic and trade activities. Due to the non-stationary and nonlinear structural characteristics of exchange rate time series, accurately predicting exchange rate movements is a challenging task. Single machine learning models often exhibit lower precision in exchange rate prediction compared to combined machine learning models. Hence, employing a combined model approach aims to enhance the predictive performance of exchange rate models. Both Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) exhibit intricate structures, making their direct integration challenging. To address this issue, an innovative weighted approach is adopted in this study, combining LSTM and ELM models and further refining the combination weights using an improved Marine Predators Algorithm. This paper encompasses both univariate and multivariate prediction scenarios, employing two distinct allocation strategies for training and testing datasets. This is done to investigate the influence of different dataset allocations on exchange rate prediction. Finally, the proposed LSTM-ELM weighted combination exchange rate prediction model is compared with SVM, Random Forest, ELM, LSTM, and LSTM-ELM average combination models. Experimental results demonstrate that the LSTM-ELM weighted combination exchange rate prediction model outperforms the others in both univariate and multivariate prediction settings, yielding higher predictive accuracy and superior fitting performance. Consequently, the LSTM-ELM weighted combination prediction model proves to be effective in exchange rate forecasting.

Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil

The quality and safety of edible vegetable oils are closely related to human life and health, meaning it is of great significance to explore the rapid detection methods of pesticide residues in edible vegetable oils. This study explored the applicability potential of substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion in soybean oils. First, we explored the impact of laser energy, delay time, and average oil film thickness on the spectral signals to identify the best experimental parameters. Afterward, we quantitatively analyzed soybean oil samples using these optimized conditions and developed a full-spectrum extreme learning machine (ELM) model. The model achieved a prediction correlation coefficient (RP2) of 0.8417, a root mean square error of prediction (RMSEP) of 167.2986, and a mean absolute percentage error of prediction (MAPEP) of 26.46%. In order to enhance the prediction performance of the model, a modeling method using the Boruta algorithm combined with the ELM was proposed. The Boruta algorithm was employed to identify the feature variables that exhibit a strong correlation with the fenthion content. These selected variables were utilized as inputs for the ELM model, with the RP2, RMSEP, and MAPEP of Boruta-ELM being 0.9631, 71.4423, and 10.06%, respectively. Then, the genetic algorithm (GA) was used to optimize the parameters of the Boruta-ELM model, with the RP2, RMSEP, and MAPEP of GA-Boruta-ELM being 0.9962, 11.005, and 1.66%, respectively. The findings demonstrate that the GA-Boruta-ELM model exhibits excellent prediction capability and effectively predicts the fenthion contents in soybean oil samples. It will be valuable for the LIBS quantitative detection and analysis of pesticide residues in edible vegetable oils.

Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model

Brain cancer deaths are significantly increased in all categories of aged persons due to the abnormal growth of brain tumor tissues in the brain. The death rate can be controlled by accurate early stage brain tumor diagnosis. The detection and classification of brain tumors play a crucial role in early diagnosis and treatment planning. Brain tumor detection and classification have become challenging and time-consuming for domain-specific radiologists and pathologists in medical image analysis. So, automatic detection and classification are essential to reduce the time of diagnosis. In recent years, machine learning classifiers have played an essential role in automatically classifying brain tumors. In this research, an approach based on an improved fuzzy factor fuzzy local information C means (IFF-FLICM) segmentation and hybrid modified harmony search and sine cosine algorithm (MHS-SCA) optimized extreme learning machine (ELM) is proposed for brain tumor detection and classification. The IFF-FLICM algorithm is utilized to accurately segment the brain’s magnetic resonance (MR) images to identify the tumor regions. The Mexican hat wavelet transform is employed for feature extraction from the segmented images. The extracted features from the segmented regions are fed into the MHS-SCA-ELM classifier for classification. The MHS-SCA is proposed to optimize the weights of the ELM model to improve the classification performance. Five distinct multimodal and unimodal benchmark functions are considered for optimization to demonstrate the robustness of the proposed MHS-SCA optimization technique. The image Dataset-255 is considered for this study. The quality measures such as SSIM and PSNR are considered for segmentation. The proposed IFF-FLICM segmentation achieved a peak signal-to-noise ratio (PSNR) of 37.24 dB and a structural similarity index (SSIM) of 0.9823. The proposed MHS-SCA-based ELM model achieved a sensitivity, specificity, and accuracy of 98.78%, 99.23%, and 99.12%. The classification performance results of the proposed MHS-SCA-ELM model are compared with MHS-ELM, SCA-ELM, and PSO-ELM models, and the comparison results are presented.

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

In this study, the focus was to produce xanthan gum from pineapple waste using Xanthomonas campestris. Six machine learning models were employed to optimize fermentation time and key metabolic stimulants (KH2PO4 and NH4NO3). The production of xanthan gum was optimized using two evolutionary optimization algorithms, particle swarm optimization, and genetic algorithm while the importance of input features was ranked using global sensitivity analysis. KH2PO4 was the most important input and was found to be beneficial for xanthan gum production, while a limited amount of nitrogen was needed. The extreme learning machine model was the most adequate for modeling xanthan gum production, predicting a maximum xanthan yield of 10.34 g/l (an 11.9 % increase over the control) at a fermentation time of 3 days, KH2PO4 of 15 g/l, and NH4NO3 of 2 g/l. This study has provided important insights into the intelligent modeling of a biostimulated process for valorizing pineapple waste.

A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM.

The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing secondary pollution of samples. In contrast, laser induced fluorescence (LIF) technology has been introduced for the identification of coal mine water sources due to its high sensitivity and real-time performance. However, extreme learning machine (ELM) have shortcomings in randomly selecting weights and biases. The Beluga Whale Optimization (BWO) algorithm has efficient optimization capability, global search capability, adaptability and parallelism, and can find the optimal weights and biases in a short time. The combination of LIF technology and BWO-ELM model can be applied to quickly identify the welling water source in coal mine. Select sandstone water and old goaf water from the Huainan mining area as experimental samples, and mix them in different proportions to prepare 7 mixed water samples for testing. Utilize LIF technology to obtain spectral curve images, preprocess them with polynomial smoothing algorithm (SG) and spectral multiple scattering correction (MSC), and perform dimensionality reduction using factor analysis (FA) and linear discriminant analysis (LDA) methods. Finally, construct ELM models, Long Short Term Memory (LSTM) models, BWO-ELM models, and Particle Swarm Optimization Extreme Learning Machine(PSO-ELM) models for the dimensionality reduced data. In order to improve the reliability and accuracy of the results, the experimental results were kept to 5 decimal places. From the experimental results, it can be seen that SG-LDA-BWO-ELM has the best fitting effect, with a fitting coefficient of 0.99990, a root mean square error of 0.00041, a mean square error approaching 0, and an average absolute error of 0.00021. It has the best convergence and the smallest absolute error among all models, making it the most suitable for identifying mine water inrush. It is of great significance for preventing and controlling mine water disasters and ensuring coal mine production safety.

Predicting mechanical properties lower upper bound for cold-rolling strip by machine learning-based artificial intelligence

The mechanical properties serve as crucial quality indicators for cold-rolled strips. For a long time, the mechanical properties mechanism and data-driven models can't comprehensively consider sufficient factors to achieve high-accuracy prediction due to the "data-isolated island" between production lines. In this research, we introduce a multi-process collaborative platform based on the industrial internet system. This platform is designed to enable real-time collection of diverse and heterogeneous data from both upstream and downstream processes of cold rolling. On this basis, a novel mechanical properties interval prediction model is proposed using the sparrow search algorithm to optimize fast learning network under the LUBE framework. We trained the model by using a dataset collected from a large steel plant. Based on the rolling theory and Pearson correlation coefficient, 25 features are selected as the inputs of the prediction model. The experimental results and comparison show that the proposed model is feasible and outperforms other machine learning models, such as the artificial bee colony algorithm optimized extreme learning machine and back propagation neural network model.

Application of terahertz Time-Domain spectroscopy and chemometrics-based whale optimization algorithm in PDE5 inhibitor detection

Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted sampling - whale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations.

Wind power error compensation prediction model based on CEEMD-SE-ELM-TCN

Abstract Wind power generation holds immense importance in addressing the issue of global energy shortage, while precise wind power forecasting proves essential for effective management and dependable operation of wind power networks. This study introduces a hybrid deep learning model, encompassing complete ensemble empirical mode decomposition (CEEMD), sample entropy (SE), extreme learning machine (ELM) and time convolutional network (TCN), for accurately predicting short-term wind power output. First, CEEMD decomposed the original wind power into multiple submodes, which effectively reduced the series volatility. Then, the SE of intrinsic mode function sequence is calculated, and the subsequences with similar complexity are superimposed to reduce the calculation cost, improve the simulation accuracy and reduce the noise of the original wind power sequence. Secondly, the ELM model is established for each submode, and the prediction error of BiLSTM is predicted again using TCN to improve the efficiency and prediction performance of the hybrid model. Finally, the outcomes of each individual submode are amalgamated to yield the ultimate prediction outcome. To showcase the efficacy and dominance of the error compensation technique, several comparison models were established in the experiment. The results demonstrated that the suggested hybrid model exhibits superior predictive accuracy in the domain of wind power prediction. Compared with the comparison model, the improvement in MAPE and RMSE was 60.50 and 77.74%, respectively.

Cotton Verticillium wilt monitoring based on UAV multispectral-visible multi-source feature fusion

Verticillium wilt seriously jeopardizes cotton growth and restricts cotton yields. Therefore, it is important to accurately, rapidly, and non-destructively estimate the extent of cotton Verticillium wilt (CVW). The focus of this study was to explore the potential of combining the vegetation index (VI), color index (CI), and texture features to improve the accuracy of CVW disease severity estimation based on hexacopter Unmanned Aerial Vehicle (UAV) images. Simple Linear Regression (LR) and Multiple Linear Regression (MLR) methods were used to determine correlations between VI, CI, texture, and normalized difference texture index (NDTI) variables and cotton Verticillium wilt disease index (DI). The LR model based on VI, CI, and NDTI was constructed, VIs, CIs, and NDTIs were fused, and Grey Wolf Optimizer (GWO) Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) Backpropagation Neural Network (BP) models were constructed to comparatively explore the estimation ability of each model on the degree of CVW disease. The results showed that VI was significantly correlated with DI, followed by NDTI, and CI. Compared with texture, NDTI containing both texture features was more significantly correlated with DI. The accuracy of the DI estimation using LR was highest for the one-factor VI model (R2 > 0.48, RRMSE < 51.48), followed by the NDTI model (R2 > 0.47, RRMS < 60.81) and the CI model (R2 > 0.33, RRMSE < 52.58). The PSO-BP and GWO-ELM were further used to model the DI estimation with different input variables. Regardless of the period, the fusion of three data sources (VIs + CIs + TIs) was preferable to a single data source or a combination of two data sources for different model inputs. In terms of different modeling algorithms, GWO-ELM combining VIs, CIs, and NDTIs had the highest estimation accuracy compared with SR and PSO-BP, with a validated R2 values of 0.65 (RRMSE = 42.96) at the flowering stage, 0.66 (RRMSE = 20.00) at the flower and boll stage, and 0.88 (RRMSE = 10.53) at the boll stage. This study demonstrated that the estimation accuracy of DI was significantly improved using collaborative modeling with multiple data sources. This study provides ideas and methods for monitoring crop disease conditions using low-altitude remote sensing.