Search Results (928)

This study predicted the potential habitats of Monochamus alternatus, a known vector of Bursaphelenchus xylophilus, utilizing its occurrence points and environmental variables—ecoclimatic indices and terrain variables. SSP2-4.5 and SSP5-8.5 scenarios were applied to predict the potential habitat under climate change. We secured the 20,514 occurrence points of Monochamus alternatus among the points with geographic coordinates of PWD-affected trees (2017–2022). The maximum entropy model (MaxEnt) and ensemble model (ensemble) were used to identify and compare the variability of potential habitats in the baseline period, near future, intermediate future, and distant future. At the outset, both the MaxEnt and the ensemble models showed a high model fit, and the ensemble was judged to be relatively superior. Next, both models showed that the habitat will expand northward according to climate change scenarios. Finally, the binary maps were superimposed to examine the differences between individual and multiple models; both models showed similar distributions in the baseline period and near future. Nonetheless, MaxEnt tended to overestimate expansion in the intermediate and far future. In the future, it is expected that the accuracy and reliability of forecasts can be improved by building optimized models to reduce uncertainty by supplementing field data and collaborating with model experts. Full article

This study introduces an innovative ensemble data-driven model designed for short-term water demand forecasting within urban areas. By synergistically combining three distinct machine learning approaches—NHiTS, XGBoost regression, and a multi-head 1D convolutional neural network—our model enhances forecasting accuracy and reliability. This integration not only leverages the unique strengths of each method but also compensates for their individual weaknesses, resulting in a robust solution for predicting urban water demand. Tested against the Battle of Water Demand Forecasting dataset (WDSA-CCWI-2024), our ensemble model demonstrates superior performance, offering a promising tool for efficient water resource management and decision making. Full article

This study presents a collaborative framework developed by the Water Futures team of researchers for the “Battle of the Water Demand Forecasting” challenge at the 3rd International WDSA-CCWI Joint Conference. The framework integrates an ensemble of machine learning forecasting models into a deterministic outcome consistent with the competition formulation. The water demand trajectory over a week exhibits complex overlapping patterns and non-linear dependencies to multiple features and time-dependent events that a single model cannot accurately predict. As such, the reconciled forecast from an ensemble of models exceeds the performance of the individual ones and exhibits higher stability across the weeks of the year and district metered areas considered. Full article

Forecasting nuclear energy production is essential for market operations such as security, economic efficiency, resource optimization, grid stability, and the integration of renewable energy sources. Forecasting approaches allow nuclear power plants to operate consistently, contributing to the overall reliability and long-term viability of the energy system. It is noted that energy systems researchers are increasingly interested in machine learning models used to face the challenge of time series forecasting. This study evaluates a hybrid ensemble learning of three time series forecasting models including least-squares support vector regression, gated recurrent unit, and long short-term memory models applied to nuclear power time series forecasting on the dataset of French power plants from 2009 to 2020. Furthermore, this research evaluates forecasting results in which approaches are directed towards the optimized RreliefF (Robust relief Feature) selection algorithm using a hyperparameter optimization based on tree-structured Parzen estimator and following an ensemble learning approach, showing promising results in terms of performance metrics. The suggested ensemble learning model, which combines deep learning and the RreliefF algorithm using a hold-out, outperforms the other nine forecasting models in this study according to performance criteria such as 75% for the coefficient of determination, a root squared error average of 0.108, and an average absolute error of 0.080. Full article

Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using the Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test and innovative trend analysis (ITA). Additionally, we utilized a hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, and the long short-term memory (LSTM) network (i.e., CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. In terms of trend analysis of the monthly total rainfall, the MK and MMK tests detected a significant decreasing trend with negative z-scores of −3.7541 and −4.0773, respectively. The ITA also indicated a significant downward trend of total monthly rainfall, especially for values between 10 and 110 mm/month. The SPI forecasting results show that the hybrid model (CEEMDAN-ARIMA-LSTM) had the highest prediction accuracy of the models at all SPI timescales. The Root Mean Square Error (RMSE) values of the CEEMDAN-ARIMA-LSTM hybrid model are 0.121, 0.044, and 0.042 for SPI-6, SPI-9, and SPI-12, respectively. The directional symmetry for this hybrid model is 0.950, 0.917, and 0.950, for SPI-6, SPI-9, and SPI-12, respectively. This indicates that this is the most suitable model for forecasting long-term drought conditions in Cape Town. Additionally, models that use a decomposition step and those that are built by combining independent models seem to produce improved SPI prediction accuracy. Full article

Short-term water demand forecasting is critical to enable optimal system operation. For practical purposes, the accuracy of the forecast and the adaptability to changing conditions are paramount. Therefore, for the Battle of Water Demand Forecasting (BWDF), we propose a precise and highly flexible forecasting methodology to allow an excellent adaptation to District Meter Areas (DMA)-specific characteristics. The proposed method consists of data cleaning and pre-processing, the training of individual forecast models and finally of combining the individual forecasts by the smoothed Bernstein Online Aggregation (BOA) algorithm. The ensemble of individual forecasting models includes simple time series, high-dimensional linear, and highly non-linear models such as neural networks. Full article

Given the increasing competition among household appliance enterprises, accurately predicting household appliance demand is crucial for enterprise supply chain management and marketing. This paper proposes a combined model integrating deep learning and ensemble learning—LSTM-RF-XGBoost—to assist enterprises in identifying customer demand, thereby addressing the complexity and uncertainty of the household appliance market demand. In this study, Long Short-Term Memory Network (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models are established separately. Then, the three individual algorithms are used as the base models in the first layer, with the multiple linear regression (MLR) algorithm serving as the meta-model in the second layer, merging the demand prediction model based on the hybrid model into the overall demand prediction model. This study demonstrates that the accuracy and stability of demand prediction using the LSTM–RF–XGBoost model significantly outperform traditional single models, highlighting the significant advantages of using a combined model. This research offers practical and innovative solutions for enterprises seeking rational resource allocation through demand prediction. Full article

Coastal upwelling is of particular importance in the western Iberian Peninsula, considering its socioeconomic impact on the region. Therefore, it is of crucial interest to evaluate how climate change, by modifying wind patterns, might influence its intensity and seasonality. Given the limited spatial extension of the area, it is essential to use high-resolution data. Thus, the weather research and forecasting model was used to dynamically downscale data from a multi-model ensemble from the 6th phase of the Coupled Model Intercomparison Project, representing the latest climate projections. Two shared socioeconomic pathways, 2–4.5 and 5–8.5 scenarios, were considered. The results show that climate change will not modify the upwelling seasonality in the area, where the months from April to September represent the period of highest intensity. Conversely, this seasonality might be exacerbated throughout the 21st century, as upwelling is expected to strengthen during these months and decrease during others. Additionally, coastal upwelling shows the highest increase at the northerner locations of the western Iberian Peninsula, resulting in a homogenization of its intensity along this coast. These changes may result from the anticipated intensification and northward shift of the Azores High. Full article

Accurate solar power generation forecasting is paramount for optimizing renewable energy systems and ensuring sustainability in our evolving energy landscape. This study introduces a pioneering approach that synergistically integrates Boosting Cascade Forest and multi-class-grained scanning techniques to enhance the precision of solar farm power output predictions significantly. While Boosting Cascade Forest excels in capturing intricate, nonlinear variable interactions through ensemble decision tree learning, multi-class-grained scanning reveals fine-grained patterns within time-series data. Evaluation with real-world solar farm data demonstrates exceptional performance, reflected in low error metrics (mean absolute error, 0.0016; root mean square error 0.0036) and an impressive R-squared score of 99.6% on testing data. This research represents the inaugural application of these advanced techniques to solar generation forecasting, highlighting their potential to revolutionize renewable energy integration, streamline maintenance, and reduce costs. Opportunities for further refinement of ensemble models and exploration of probabilistic forecasting methods are also discussed, underscoring the significance of this work in advancing solar forecasting techniques for a sustainable energy future. Full article

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model. Full article

High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods. Full article

Spatiotemporal axle temperature forecasting is crucial for real-time failure detection in locomotive control systems, significantly enhancing reliability and facilitating early maintenance. Motivated by the need for more accurate and reliable prediction models, this paper proposes a novel ensemble graph convolutional recurrent unit network. This innovative approach aims to develop a highly reliable and accurate spatiotemporal axle temperature forecasting model, thereby increasing locomotive safety and operational efficiency. The modeling structure involves three key steps: (1) the GCN module extracts and aggregates spatiotemporal temperature data and deep feature information from the raw data of different axles; (2) these features are fed into GRU and BiLSTM networks for modeling and forecasting; (3) the ICA algorithm optimizes the fusion weight coefficients to combine the forecasting results from GRU and BiLSTM, achieving superior outcomes. Comparative experiments demonstrate that the proposed model achieves RMSE values of 0.2517 °C, 0.2011 °C, and 0.2079 °C across three temperature series, respectively, indicating superior prediction accuracy and reduced errors compared to benchmark models in all experimental scenarios. The Wilcoxon signed-rank test further confirms the statistical significance of the result improvements with high confidence. Full article

For several years, a growing interest among numerous researchers and investors in predicting stock price movements has spurred extensive exploration into employing advanced deep learning models. These models aim to develop systems capable of comprehending the stock market’s complex nature. Despite the immense challenge posed by the diverse factors influencing stock price forecasting, there remains a notable lack of research focused on identifying the essential feature set for accurate predictions. In this study, we propose a Dynamic Feature Selection System (DFSS) to predict stock prices across the 10 major industries, as classified by the FnGuide Industry Classification Standard (FICS) in South Korea. We apply 16 feature selection algorithms from filter, wrapper, embedded, and ensemble categories. Subsequently, we adjust the settings of industry-specific index data to evaluate the model’s performance and robustness over time. Our comprehensive results identify the optimal feature sets that significantly impact stock prices within each sector at specific points in time. By analyzing the inclusion ratios and significance of the optimal feature set by category, we gain insights into the proportion of feature classes and their importance. This analysis ensures the interpretability and reliability of our model. The proposed methodology complements existing methods that do not consider changes in the types of variables significantly affecting stock prices over time by dynamically adjusting the input variables used for learning. The primary goal of this study is to enhance active investment strategies by facilitating the creation of diversified portfolios for individual stocks across various sectors, offering robust models and feature sets that consistently demonstrate high performance across industries over time. Full article

With the continuous increase in the proportion of distributed photovoltaic power stations, the demand for photovoltaic power grid connection is becoming more and more urgent, and the requirements for the accuracy of regional distributed photovoltaic power forecasting are also increasing. A distributed regional photovoltaic power prediction model based on a stacked ensemble algorithm is proposed here. This model first uses a graph attention network (GAT) to learn the structural features and relationships between sub-area photovoltaic power stations, dynamically calculating the attention weights of the photovoltaic power stations to capture the global relationships and importance between stations, and selects representative stations for each sub-area. Subsequently, the CNN-LSTM-multi-head attention parallel multi-channel (CNN-LSTM-MHA (PC)) model is used as the basic model to predict representative stations for sub-areas by integrating the advantages of both the CNN and LSTM models. The predicted results are then used as new features for the input data of the meta-model, which finally predicts the photovoltaic power of the large area. Through comparative experiments at different seasons and time scales, this distributed regional approach reduced the MAE metric by a total of 22.85 kW in spring, 17 kW in summer, 30.26 kW in autumn, and 50.62 kW in winter compared with other models. Full article

The prediction of the containerized freight index has important economic and social significance. Previous research has mostly applied sub-predictors directly for integration, which cannot be optimized for different datasets. To fill this research gap and improve prediction accuracy, this study innovatively proposes a new prediction model based on adaptive model selection and multi-objective ensemble to predict the containerized freight index. The proposed model comprises the following four modules: adaptive data preprocessing, model library, adaptive model selection, and multi-objective ensemble. Specifically, an adaptive data preprocessing module is established based on a novel modal decomposition technology that can effectively reduce the impact of perturbations in historical data on the prediction model. Second, a new model library is constructed to predict the subseries, consisting of four basic predictors. Then, the adaptive model selection module is established based on Lasso feature selection to choose valid predictors for subseries. For the subseries, different predictors can produce different effects; thus, to obtain better prediction results, the weights of each predictor must be reconsidered. Therefore, a multi-objective artificial vulture optimization algorithm is introduced into the multi-objective ensemble module, which can effectively improve the accuracy and stability of the prediction model. In addition, an important discovery is that the proposed model can acquire different models, adaptively varying with different extracted data features in various datasets, and it is common for multiple models or no model to be selected for the subseries.The proposed model demonstrates superior forecasting performance in the real freight market, achieving average MAE, RMSE, MAPE, IA, and TIC values of 9.55567, 11.29675, 0.44222%, 0.99787, and 0.00268, respectively, across four datasets. These results indicate that the proposed model has excellent predictive ability and robustness. Full article