Machine learning approaches for wind speed forecasting using long-term monitoring data: a comparative study

A dose deposition matrix (DDM) prediction method using several voxel features and a machine learning (ML) approach is proposed for plan optimization in radiation therapy. Head and lung cases with the inhomogeneous medium are used as training and testing data. The prediction model is a cascade forward backprop neural network where the input is the features of the voxel, including 1) voxel to body surface distance along the beamlet axis, 2) voxel to beamlet axis distance, 3) voxel density, 4) heterogeneity corrected voxel to body surface distance, 5) heterogeneity corrected voxel to beamlet axis, and (6) the dose of voxel obtained from the pencil beam (PB) algorithm. The output is the predicted voxel dose corresponding to a beamlet. The predicted DDM was used for plan optimization (ML method) and compared with the dose of MC-based plan optimization (MC method) and the dose of pencil beam-based plan optimization (PB method). The mean absolute error (MAE) value was calculated for full volume relative to the dose of the MC method to evaluate the overall dose performance of the final plan. For patient with head tumor, the ML method achieves MAE value 0.49 × 10-4 and PB has MAE 1.86 × 10-4. For patient with lung tumor, the ML method has MAE 1.42 × 10-4 and PB has MAE 3.72 × 10-4. The maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 1.2% for patient with head tumor, while the difference is larger than 10% using the PB method. For patient with lung tumor, the maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 2.1%, while the difference is larger than 16% using the PB method. In this work, a reliable DDM prediction method is established for plan optimization by applying several voxel features and the ML approach. The results show that the ML method based on voxel features can obtain plans comparable to the MC method and is better than the PB method in achieving accurate dose to the patient, which is helpful for rapid plan optimization and accurate dose calculation. Establishment of a new machine learning method based on the relationship between the voxel and beamlet features for dose deposition matrix prediction in radiation therapy.

The aim of this work was to develop an accurate model to swiftly forecast the ultimate conditions and the stress–strain curves of ultra-high-performance concrete (UHPC) confined by fibre-reinforced polymer (FRP). Four neural network predictive models based on machine learning (back-propagation neural network (BPNN), support vector regression (SVR), extreme learning machine (ELM) and generalised regression neural network (GRNN)) were constructed to forecast the ultimate conditions. A BPNN-based model to predict stress–strain curves was developed. A database of 193 compression test results of FRP-confined UHPC members was collected from the open literature and a thorough evaluation of data quality was conducted. The results showed that the SVR, BPNN, GRNN and ELM prediction models exhibited excellent accuracy when compared with a design-oriented model. The GRNN exhibited the highest predictive precision, followed by SVR. The developed prediction models establish a foundation for the design and quick prediction of FRP-confined UHPC.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

The current mainstream new energy vehicle energy management strategies are mainly designed for a specific working condition without considering the uncertainty of actual driving cycles. Therefore, online identification of driving cycles can lay the foundation for formulating optimal energy management strategies. In this paper, representative driving cycles are selected from the standard library and processed accordingly. Secondly, the K-Means algorithm is used to perform cluster analysis on the processed driving cycles, and two types of comprehensive driving cycle databases were constructed. Then, the driving cycle recognition model based on BP neural network, extreme learning machine (ELM), and generalized regression neural network (GRNN) is constructed in the MATLAB environment to find the driving cycle recognition strategy with high recognition accuracy. Finally, the simulation results show that the operating driving cycle’s recognition model based on the generalized regression neural network (GRNN) can better identify the driving cycles online than the other two types.

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

Real-time porosity prediction using gas-while-drilling data and machine learning with reservoir associated gas: Case study for Hassi Messaoud field, Algeria

Application of small sample virtual expansion and spherical mapping model in wind turbine fault diagnosis

Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data

Multiple faults are easily confused with single faults. In order to identify multiple faults more accurately, a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine, called DPT-ELM. The DPT-ELM method is a variant of an extreme learning machine (ELM). There are some issues with ELM. First, achieving a high accuracy requires too many hidden nodes; second, the direct connection between the input layer and the output layer is ignored. Accordingly, to deal with the above-mentioned problems, DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM, which can achieve a desired performance with fewer hidden nodes. In addition, a direct connection is built between the input layer and the output layer. Since the input layer weights and the thresholds of the two hidden layers are determined randomly, this simplifies the improved model and shortens the calculation time. Additionally, to improve the signal to noise ratio (SNR), an adaptive waveform decomposition (AWD) algorithm is used to denoise the vibration signal. Then, the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods. Finally, the eigenvalues are input to the DPT-ELM classifier. In this paper, two groups of rolling bearing data at different speeds, which were collected from a real experimental platform, are used to test the method. Each set of data includes three single fault states, two complex fault states and a healthy state. The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy. Moreover, based on 10-fold cross-validation, it proves to be an effective method to improve the accuracy with fewer hidden nodes.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Surrogate-based multiobjective optimization to rapidly size low impact development practices for outflow capture

A machine learning approach for hierarchical classification of software requirements

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point–surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71–0.83 and RMSEs of 0.025–0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.

In this study, 100 groups of apples with different sweetness were measured in transmission mode using visible light spectroscopy (VIS). The absorption spectra of all samples were obtained in the wavelength range of 400-800 nm with a step of 5 nm. To classify and identify the sweetness of apples, a qualitative classification model of apple absorption spectra and sweetness was constructed using BP neural network. The sweetness of all apples was classified into three different classes and labeled with Arabic numbers from one to three. In the experiment, 80 groups of apples were randomly selected as training samples and 20 groups of apples as test samples. Through the test, the sweetness classification accuracy of the test samples based on BP neural network reached 75%. To further improve the classification accuracy of sweetness, a Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the BP neural network. With the optimal values of BP-PSO model parameters, the sweetness classification accuracy reached 90% for 20 sets of test samples. Finally, traditional classification models of extreme learning machine (ELM), competitive neural network (CNN) and self-organizing mapping neural network (SOMNN) were established to compare the classification accuracy of different algorithms, and the accuracy of 50%, 35% and 65% was achieved using ELM, CNN and SOMNN models, respectively. The results show that the classification using BP-PSO model has higher classification accuracy. Therefore, the BP-PSO model can be applied to the quality identification and classification of apples based on VIS technique.