Search Results (4,317)

Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R² (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies. Full article

Background/Objectives: While multiple studies have investigated antibiotic consumption rates, there are few studies on the consumption of systemic antifungals and antiprotozoals. This study aims to fill this gap by providing a comprehensive analysis of nationwide consumption trends in Kazakhstan over a seven-year period (2017–2023). Methods: Defined daily doses per 1000 inhabitants per day were calculated for systemic antifungals (J02 code of the Anatomical Therapeutic Chemical Classification System (ATC)) and antiprotozoals (P01 code of the ATC). Time series analyses were applied to examine historical trends, evaluate the impact of the COVID−19 pandemic, and make future projections until 2030. Results: The total consumption increased over the study period, with an average annual percent change of 1.11% for antifungals and 5.48% for antiprotozoals. Fluconazole was the most consumed antifungal agent, whereas metronidazole was the most consumed antiprotozoal agent. The COVID−19 pandemic had a positive but insignificant effect on the consumption of antifungals and a negative and also insignificant effect on the consumption of antiprotozoals. Forecast modeling indicates that the future trends in antifungal and antiprotozoal consumption until 2030 will largely remain stable, with the exception of antiprotozoal consumption in the hospital sector, which is projected to decline. Conclusions: These findings offer valuable insights into the development and implementation of targeted antimicrobial stewardship programs in Kazakhstan. Full article

In this study, we introduce an innovative method for load forecasting that capitalizes on the concept of task affinity score to measure the similarity between various tasks. The task affinity score emerges as a superior technique for assessing task similarity within the realm of transfer learning. Through empirical evaluation on a synthetic dataset, we establish the superiority of the task affinity score over traditional metrics in task selection scenarios. To operationalize this method, we unveil the Affinity-Driven Transfer Learning (ADTL) algorithm to enhance load forecasting precision. The ADTL algorithm enriches the transfer learning framework by incorporating insights from both pre-trained models and datasets, thereby augmenting the accuracy of load forecasting for new and unseen datasets. The robustness of the ADTL algorithm is further evidenced through its application to two empirical datasets, namely the dataset provided by the Australian Energy Market Operator (AEMO) and the Smart Australian dataset. In conclusion, our research underscores the important role of the task affinity score in refining transfer learning methodologies for load forecasting applications. Full article

In the realm of artificial intelligence, knowledge graphs (KGs) serve as an essential structured framework, capturing intricate relationships between diverse entities and supporting a broad spectrum of AI applications. Despite their utility, the static characteristic of KGs poses challenges in dynamically evolving information landscapes. This has catalyzed the development of temporal knowledge graphs (TKGs), which introduce a temporal layer to KGs, facilitating the representation of knowledge progression through time. This study zeroes in on the critical task of TKG extrapolation, which is vital for forecasting future occurrences and offering foresight into emerging situations across a variety of fields. Most contemporary approaches to TKG extrapolation are predicated on the symmetrical encoder–decoder paradigm, wherein the processes of representation learning and reasoning are harmoniously intertwined. While the encoder often garners the most attention due to its role in capturing and encoding information, the pivotal role of the decoder, which is often overlooked, is essential for direct inference and the accurate projection of temporal dynamics. To this end, we present the Householder-transformation-based temporal knowledge graph extrapolation (HTKGE) method: a groundbreaking encoder–decoder framework that reimagines the decoder’s contribution to TKG extrapolation. Our approach spotlights an adaptive decoder propelled by Householder transformations, which engage dynamically with the temporal encoding from the encoder. This interaction fosters a nuanced comprehension of the TKG’s temporal trajectory. Our empirical evaluations across four benchmark TKG datasets substantiate HTKGE’s consistent efficacy in TKG extrapolation tasks. Full article

This study employed the new generation Taiwan global forecast system (TGFS) to focus on its performance in forecasting the tracks of western North Pacific typhoons during 2022–2023. TGFS demonstrated better forecasting performance in typhoon track compared to central weather administration (CWA) GFS. For forecasts with large track errors by TGFS at the 120th h, it was found that most of them originated during the early stages of typhoon development when the typhoons were of mild intensity. The tracks deviated predominantly towards the northeast and occasionally towards the southwest, which were speculated to be due to inadequate environmental steering guidance resulting from the failure to capture synoptic environmental features. The tracks could be corrected by replacing the original new simplified Arakawa–Schubert (NSAS) scheme with the new Tiedtke (NTDK) scheme to change the synoptic environmental field, not only for Typhoon Khanun, which occurred in the typhoon season of 2023, but also for Typhoon Bolaven, which occurred after the typhoon season, in October 2023, under atypical circulation characteristics over the western Pacific. The diagnosis of vorticity budget primarily analyzed the periods where divergence in typhoon tracks between control (CTRL) and NTDK experiments occurred. The different synoptic environmental fields in the NTDK experiment affected the wavenumber-1 vorticity distribution in the horizontal advection term, thereby enhancing the accuracy of typhoon translation velocity forecasts. This preliminary study suggests that utilizing the NTDK scheme might improve the forecasting skill of TGFS for typhoon tracks. To gain a more comprehensive understanding of the impact of NTDK on typhoon tracks, further examination for more typhoons is still in need. Full article

Rainfall variability and its underlying physical mechanisms are crucial for improving the predictive accuracy of July rainfall patterns in the Sichuan-Shaanxi (SS) region of Southwestern China. This study utilized observational 24 h accumulated rainfall data from China in conjunction with reanalysis products sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The purpose of this study was to elucidate the relationship between daily variations in the daily Asian-Pacific Oscillation (APO), atmospheric circulation, and daily rainfall patterns in the SS region, and to evaluate the impact of atmospheric circulation anomalies on these relationships. The results reveal a discernible intensification in the sea–land thermal contrast associated with atmospheric circulation anomalies transitioning from the daily extremely low APO (ELA) to the extremely high APO (EHA) days. These conditions lead to an increased presence of water vapor and widespread anomalies in rainfall that exceed normal levels in the SS region. Concurrently, the increase in stations experiencing extreme rainfall events (EREs) accounts for 21.3% of the overall increase in stations experiencing rainfall. The increase in rainfall amount contributed by EREs (RA-EREs) accounts for 73.5% of the overall increase in the total rainfall amount (TRA) across the SS region. Specifically, heavy rainfall (HR) and downpour rainfall (DR) during EREs accounted for 65.7% (HR) and 95.3% (DR) of the overall increase in the TRA, respectively. Relative to the ELA days, there was a substantial 122.6% increase in the occurrence frequency of EREs and a 23.3% increase in their intensity. The study suggests that the daily APO index emerges as a better indicator of July rainfall events in the SS region, with EREs significantly contributing to the overall increase in rainfall in this region. These findings indicate the importance of improving predictive capabilities for daily variability in the APO index and their correlation with rainfall events in the SS region. The results may inform the development of effective adaptation and mitigation strategies to manage the potential impacts of EREs on agriculture, water resources, sustainable development, and infrastructure in the region. Full article

Short-term load forecasting is a challenging research problem and has a tremendous impact on electricity generation, transmission, and distribution. A robust forecasting algorithm can help power system operators to better tackle the ever-changing electric power demand. This paper presents a novel deep neural network for short-term electric load forecasting for the St. John’s campus of Memorial University of Newfoundland (MUN). The electric load data are obtained from the Memorial University of Newfoundland and combined with metrological data from St. John’s. This dataset is used to formulate a multivariate time-series forecasting problem. A novel deep learning algorithm is presented, consisting of a 1D Convolutional Neural Network, which is followed by an encoder–decoder-based network with attention. The input used for this model is the electric load consumption and metrological data, while the output is the hourly prediction of the next day. The model is compared with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM)-based Recurrent Neural Network. A CNN-based encoder–decoder model without attention is also tested. The proposed model shows a lower mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and higher R² score. These evaluation metrics show an improved performance compared to GRU and LSTM-based RNNs as well as the CNN encoder–decoder model without attention. Full article

The increasing adoption of renewable energy, particularly photovoltaic (PV) power, has highlighted the importance of accurate PV power forecasting. Despite advances driven by deep learning (DL), significant challenges remain, particularly in capturing the long-term dependencies essential for accurate forecasting. This study presents TransPVP, a novel transformer-based methodology that addresses these challenges and advances PV power forecasting. TransPVP employs a deep fusion technique alongside a multi-task joint learning framework, effectively integrating heterogeneous data sources and capturing long-term dependencies. This innovative approach enhances the model’s ability to detect patterns of PV power variation, surpassing the capabilities of traditional models. The effectiveness of TransPVP was rigorously evaluated using real data from a PV power plant. Experimental results showed that TransPVP significantly outperformed established baseline models on key performance metrics including RMSE, R², and CC, underscoring its accuracy, predictive power, and reliability in practical forecasting scenarios. Full article

With the development of unconventional natural gas resources, plunger gas lift technology has gained widespread application. Accurately predicting gas production from unconventional gas reservoirs is a crucial step in evaluating the effectiveness of plunger gas lift technology and optimizing its design. However, most existing prediction methods are mechanism-driven, incorporating numerous assumptions and simplifications that make it challenging to fully capture the complex physical processes involved in plunger gas lift technology, ultimately leading to significant errors in capacity prediction. Furthermore, engineering design factors and production system factors associated with plunger gas lift technology can contribute to substantial deviations in gas production forecasts. This study employs three powerful regression algorithms, XGBoost, Random Forest, and SVR, to predict gas production in plunger gas lift wells. This method comprehensively leverages various types of data, including collected engineering design, production system, and production data, directly extracting the underlying patterns within the data through machine learning algorithms to establish a prediction model for gas production in plunger gas lift wells. Among these, the XGBoost algorithm stands out due to its robustness and numerous advantages, such as high accuracy, ability to effectively handle outliers, and reduced risk of overfitting. The results indicate that the XGBoost algorithm exhibits impressive performance, achieving an R² (coefficient of determination) value of 0.87 for six-fold cross-validation and 0.85 for the test set. Furthermore, to address the “black box” problem (the inability to know the internal working structure and workings of the model and to directly understand the decision-making process), which is commonly associated with conventional machine learning models, the SHAP (Shapley additive explanations) method was utilized to globally and locally interpret the established machine learning model, analyze the main factors (such as starting time of wells, gas–liquid ratio, catcher well inclination angle, etc.) influencing gas production, and enhance the credibility and transparency of the model. Taking plunger gas lift wells in southwest China as an example, the effectiveness and practicality of this method are demonstrated, providing reliable data support for shale gas production prediction, and offering valuable guidance for actual on-site production. Full article

Forecasting the future levels of air pollution provides valuable information that holds importance for the general public, vulnerable populations, and policymakers. High-quality data are essential for precise and reliable forecasts and investigations of air pollution. Missing observations arise when the sensors utilized for assessing air quality parameters experience malfunctions, which result in erroneous measurements or gaps in the dataset and hinder the data quality. This research paper presents a novel approach for imputing missing values in air quality data in a univariate approach. The algorithm employs the random forest (RF) algorithm to impute missing observations in a bi-directional (forward and reverse in time) manner for air quality (particulate matter less than 2.5 μm (PM_2.5)) data from the Republic of Serbia. The algorithm was evaluated against simple methods, such as the mean and median imputation methods, for missing observations over durations of 24, 48, and 72 h. The results indicate that our algorithm yielded comparable error rates to the median imputation method for all periods when imputing the PM_2.5 data. Ultimately, the algorithm’s higher computational complexity proved itself as not justified considering the minimal error decrease it achieved compared with the simpler methods. However, for future improvement, additional research is needed, such as utilizing low-code machine learning libraries and time-series forecasting techniques. Full article

Climate change is affecting water resources and other aspects of life in many countries, generating more frequent extreme events. Users react to intermittent supply by implementing local private tanks to collect as much water resources as possible to cope with water distribution suspension periods. Such tanks are commonly overdesigned due to the common perception that water resources are essential for human activities and the general need of users to safeguard their water supplies. This study evaluated the impact of water scarcity and users’ self-adaptation strategies on water demand under intermittent flow conditions by implementing an experimental campaign in a real network. The analysis was conducted using a short-term water demand forecast model that reproduces periodic patterns observed at an annual, weekly and daily level to evaluate the adaptation response of users concerning the scarcity of water resources through a comparison between the real pattern of the network and the pattern of local tanks. Full article

Renovation of buildings has become a major area of development for the construction industry. In the building construction sector, generating a precise and trustworthy cost estimate before building begins is the greatest challenge. Emphasizing the value of using ANN models to forecast the total cost of a building renovation project is the ultimate objective. As a result, building firms may be able to avoid financial losses as long as there is as little discrepancy between projected and actual costs for remodeling works in progress. To address the gap in the research, Greek contractors specializing in building renovations provided a sizable dataset of real project cost data. To build cost prediction ANNs, the collected data had to be organized, assessed, and appropriately encoded. The network was developed, trained, and tested using IBM SPSS Statistics software 28.0.0.0. The dependent variable is the final cost. The independent variables are initial cost, estimated completion time, actual completion time, delay time, initial and final demolition-drainage costs, cost of expenses, initial and final plumbing costs, initial and final heating costs, initial and final electrical costs, initial and final masonry costs, initial and final construction costs of plasterboard construction, initial and final cost of bathrooms, initial and final cost of flooring, initial and final cost of frames, initial and final cost of doors, initial and final cost of paint, and initial and final cost of kitchen construction. The first procedure that was employed was the radial basis function (RBF). The efficiency of the RBFNN model was evaluated and analyzed during training and testing, with up to 6% sum of squares error and nearly 0% relative error in the training sample, which accounted for roughly 70% of the total sample. The second procedure implemented was the method called the multi-layer perceptron (MLP). The efficiency of the MLPNN model was assessed and examined during training and testing; the training sample, which made up around 70% of the overall sample, had a relative error of 0–7% and a sum of squares error ranging from 1% to 5%, confirming specifically the efficacy of RBFNN in calculating the overall cost of renovations. 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

Accurate short-term load forecasting is critical for enhancing the reliability and stability of regional smart energy systems. However, the inherent challenges posed by the substantial fluctuations and volatility in electricity load patterns necessitate the development of advanced forecasting techniques. In this study, a novel short-term load forecasting approach based on a two-stage feature extraction process and a hybrid inverted Transformer model is proposed. Initially, the Prophet method is employed to extract essential features such as trends, seasonality and holiday patterns from the original load dataset. Subsequently, variational mode decomposition (VMD) optimized by the IVY algorithm is utilized to extract significant periodic features from the residual component obtained by Prophet. The extracted features from both stages are then integrated to construct a comprehensive data matrix. This matrix is then inputted into a hybrid deep learning model that combines an inverted Transformer (iTransformer), temporal convolutional networks (TCNs) and a multilayer perceptron (MLP) for accurate short-term load forecasting. A thorough evaluation of the proposed method is conducted through four sets of comparative experiments using data collected from the Elia grid in Belgium. Experimental results illustrate the superior performance of the proposed approach, demonstrating high forecasting accuracy and robustness, highlighting its potential in ensuring the stable operation of regional smart energy systems. Full article

This study examines the frequency of tropical cyclones in the Bay of Bengal and their impact on Bangladesh. The extent of environmental harm led to the selection of two specific areas: the Panpatty Union and Galachipa Upzilla in the Patuakhali district, and the Sariakat Union and Swandip Upzilla in the Chittagong district. The results indicate that cyclonic storms are more common in May and November. The results also demonstrate that the studied regions are vulnerable to the effects of tropical cyclones and suffer significant consequences. The differences in influence between the two locations are statistically significant with a confidence level of 90%. The findings have significant ramifications for policymaking decisions. Full article