Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked ...or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales.
We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data.
Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product.
The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.
Solar energy is highly economical and widespread in new energy applications, and analyzing solar radiation information is an important part of solar photovoltaic power applications. However, because ...of its data complexity and difficulty to measure, solar radiation data needs to be predicted. Temporal Convolutional Network (TCN) model is used to extract features and Artificial Ecosystem-based Optimization (AEO) algorithm is used to optimize the parameters of TCN. Out of consideration for the phenomenon of strong fluctuations and complex features of solar radiation data, the optimal variational mode decomposition (OVMD) method is incorporated into the model. First, the signal decomposition is performed on the original data to obtain several subsequences, and then aggregated by fuzzy entropy to reduce the number of sequences, after which the data are fed into the TCN model and the model parameters are optimized using the improved AEO algorithm, and finally the results of the model prediction are the output. Four months of solar radiation data are selected for testing, it is finally concluded that the OVMD-IAEO-TCN model can be used for solar radiation prediction with higher accuracy and reliability than the other nine comparison models.
•A novel hybrid approach is proposed for solar radiation prediction.•Optimal VMD method and fuzzy entropy are used to decomposing time series.•The improved AEO algorithm is introduced to optimize the TCN model.•The hyperparameters of TCN are discussed in detail.•Eight benchmark models are used to verify the performance of the proposed model.
Magnetotelluric (MT) has significant value in earthquake prediction, space weather monitoring, mineral resources exploration, and deep earth structure detection. However, due to the complexity of the ...environment, MT data collected often are of low data quality due to noise pollution. The noisy data seriously affects the accuracy of the detection of underground structures. Therefore, we propose a magnetotelluric noise suppression method based on a coordinate attention-temporal convolutional network (CA-TCN). Firstly, the CA-TCN is trained with a large data set of artificially created data to learn the nonlinear mapping relationship between the noisy data and noise contours. Then, the CA-TCN model achieves the mapping transformation from noisy data to noise contours in the MT data. Finally, we subtract the noise contours obtained from the CA-TCN mapping model from the original noisy data to achieve signal-to-noise separation and reconstruct high quality data.In simulated experiments, the similarity between denoised data and known high quality data from Qinghai reaches 98%. The results demonstrate that the proposed method exhibits significant advantages compared to convolutional neural network (CNN) methods etc. These findings validate the feasibility of the proposed approach. We applied the proposed method to the real measured data collected from the LuZong mining area, resulting in smoother and more continuous apparent resistivity curves. This indicates that noise in the MT data has been effectively removed, leading to a significant improvement in the quality of the MT data.
Due to a small proportion of observations, reliable and accurate flood forecasts for large floods present a fundamental challenge to artificial neural network models, especially when the forecast ...horizons exceed the flood concentration time of a river basin. This study proposed for the first time a Similarity search-based data-driven framework, and takes the advanced Temporal Convolutional Network based Encoder-Decoder model (S-TCNED) as an example for multi-step-ahead flood forecasting. A total of 5232 hourly hydrological data were divided into two datasets for model training and testing. The input sequence of the model included hourly flood flows of a hydrological station and rainfall data (traced back to the previous 32 h) of 15 gauge stations, and the output sequence stepped into 1- up to 16-hour-ahead flood forecasts. A conventional TCNED model was also built for comparison purposes. The results demonstrated that both TCNED and S-TCNED could make suitable multi-step-ahead flood forecasts, while the proposed S-TCNED model not only could effectively mimic the long-term rainfall-runoff relationship but also could provide more reliable and accurate forecasts of large floods than the TCNED model even in extreme weather conditions. There is a significant positive correlation between the mean sample label density improvement and the mean Nash-Sutcliffe Efficiency (NSE) improvement of the S-TCNED over the TCNED at the long forecast horizons (13 h up to 16 h). Based on the analysis of the sample label density, it is found that the similarity search largely improves the model performance by enabling the S-TCNED model to learn the development process of similar historical floods in a targeted manner. We conclude that the proposed S-TCNED model that converts and associates the previous rainfall-runoff sequence with the forecasting runoff sequence under a similar scenario can enhance the reliability and accuracy of flood forecasts while extending the length of forecast horizons.
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•A similarity search-based data-driven framework promotes extreme flood forecasts.•The S-TCNED model improves forecast accuracy at long forecast horizons and high flows.•The S-TCNED model can provide accurate and reliable multi-step-ahead flood forecasts.•The sample label density indicator can feature the advantages of similarity search methods.
Accurate estimation of the state of health (SOH) of lithium-ion batteries is the key to ensure the safe use of lithium-ion batteries. In practice, the application of traditional health features is ...hindered by incomplete charge and discharge. When the battery is stably charged, the voltage and temperature of the battery under different health states show similar spatial degradation trends. Therefore, the degradation trend of voltage and temperature can be directly used as the health feature sequence to reduce the error caused by manual feature extraction. In addition, a new model attention depthwise temporal convolutional network (AD-TCN) considering health characteristics is proposed for SOH estimation. Depthwise separable convolution operation is used to extend temporal convolutional network (TCN) to a model suitable for multivariate prediction. Depthwise convolution is used as feature extractor, and pointwise convolution recombines all features for regression prediction. In addition, the convolutional block attention module is used in the channel dimension and spatial dimension to selectively enhance or suppress the details. Experiments on NASA data sets show that this method has strong reliability and high prediction accuracy.
•A temporal convolution network model is proposed for SOH estimation.•Trends in voltage and temperature degradation during charging as feature inputs directly•Convolutional attention mechanism is used to enhance or suppress detailed features.•Depthwise separable convolution is used to extend TCN model to multivariate prediction model.
•The proposed self-attention temporal convolutional network is applied to enhance feature extraction of wind power.•The meteorological factors are considered in ultra short-term wind power ...prediction.•The proposed method can improve the accuracy of wind power prediction by experiments.
Accurate and reliable wind power forecasting has become very important to power system scheduling and safely stable operating. In this paper, a novel self-attention temporal convolutional network (SATCN) is combined with long-short term memory (LSTM) to forecast wind power for guaranteeing the continuous electricity supply. In the proposed SATCN-LSTM model, the structure of SATCN with a self-attention mechanism is conducted to pay more attention to features that contribute more to the output. The strength of SATCN is performed through extracting temporal feature of meteorological data and correlation characteristics between variables. LSTM is used after SATCN to further build the connection between features and outputs for predicting future ultra-short time wind power. The effectiveness and advancement of the proposed method is tested by using meteorological data and wind power data from two different wind farms in the U.S. The experimental results reveal that the SATCN-LSTM model is more accurate comparing to other methods. Taking California's fourth quarter wind power forecast results as an example, the proposed method has carried out a reduction of 17.56%, 10.99%,11.34% and 3.68% on the root mean square error compared with LSTM, TCN, CNN-LSTM, TCN-LSTM.
Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally ...control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.
Short-term (less than 1 h) forecast of the power generated by wind turbines in a wind farm is extremely challenging due to the lack of reliable data from meteorological towers and numerical weather ...model outputs at these timescales. A robust deep learning model is developed for short-term forecasts of wind turbine generated power in a wind farm using the state-of-the-art temporal convolutional networks (TCN) to simultaneously capture the temporal dynamics of the wind turbine power and relationship among the local meteorological variables. An orthogonal array tuning method based on the Taguchi design of experiments is utilized to optimize the hyperparameters of the proposed TCN model. The proposed TCN model is validated using twelve months of data from a 130 MW utility-scale wind farm with 86 wind turbines in comparison with some of the existing methods in the literature. The power curves obtained from the proposed TCN model show consistent improvements over existing methods at all wind speeds.
•Deep learning model based on state-of-the-art TCN to predict the total wind power.•Multi-step prediction of wind power for 0, 10, 20, 30, 40 and 50 min ahead.•OATM to optimize the hyperparameters of the deep learning models.•Comparison of multi-step ahead TCN model against LSTM, CNN + LSTM and MLR models.
This paper presents a novel model reduction method based on proper orthogonal decomposition and temporal convolutional neural network. The method generates basis functions of the flow field by proper ...orthogonal decomposition, and the coefficients are taken as the low-dimensional features. Temporal convolutional neural network is used to construct the model for predicting low-dimensional features. In this work, the training data are obtained from high fidelity numerical simulation. Compared with recurrent networks, temporal convolutional neural network is more effective with fewer parameters. The model reduction method developed here depends only on the solution of flow field. The performance of the new reduced order model is evaluated using numerical case: flow past a cylinder. Experimental results illustrate that time cost is reduced by three orders of magnitude, and convolutional architecture is beneficial to construct reduced order model. The speed-up ratio is linear with the computational scale of the numerical simulation.
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