Guebin Choi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073985 - 29 January 2026

Abstract This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series. Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks (STGNNs). However, such definitions are prone to generating spurious correlations due to the dominance of periodic structures. To address this limitation, we adopt the Elastic-Band Transform (EBT) to decompose solar radiation into periodic and amplitude-modulated components, which are then modeled independently with separate graph neural networks. The periodic component, characterized by strong More >

Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >

Zhongrui Jing¹, Hongzhang Yang^1,*, Jiangpu Guo²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.067759 - 09 December 2025

Abstract Hard disk drives (HDDs) serve as the primary storage devices in modern data centers. Once a failure occurs, it often leads to severe data loss, significantly degrading the reliability of storage systems. Numerous studies have proposed machine learning-based HDD failure prediction models. However, the Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes differ across HDD manufacturers. We define hard drives of the same brand and model as homogeneous HDD groups, and those from different brands or models as heterogeneous HDD groups. In practical engineering scenarios, a data center is often composed of a heterogeneous population of… More >

Jeongsu Park¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3913-3948, 2025, DOI:10.32604/cmes.2025.072261 - 23 December 2025

Abstract Time series anomaly detection is critical in domains such as manufacturing, finance, and cybersecurity. Recent generative AI models, particularly Transformer- and Autoencoder-based architectures, show strong accuracy but their robustness under noisy conditions is less understood. This study evaluates three representative models—AnomalyTransformer, TranAD, and USAD—on the Server Machine Dataset (SMD) and cross-domain benchmarks including the Soil Moisture Active Passive (SMAP) dataset, the Mars Science Laboratory (MSL) dataset, and the Secure Water Treatment (SWaT) testbed. Seven noise settings (five canonical, two mixed) at multiple intensities are tested under fixed clean-data training, with variations in window, stride, and More > Graphic Abstract

Manzhu Liao¹, Lan Yang¹, Liehua Tie¹, Qiqiang Guo^1,*, Weilie Zheng^2,*, Jiangrong Li², Yongxia Li²

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3633-3652, 2025, DOI:10.32604/phyton.2025.072725 - 01 December 2025

Abstract Cupressus gigantea is an endemic endangered tree species in the Tibet Plateau, and studying the survival status of the different C. gigantea populations and revealing the main environmental factors that affect the population survival are particularly significant for the conservation and sustainable development of endangered species. Based on the 28 sample plots, the Hierarchical Cluster Method was used to classify the C. gigantea populations into four community types. Age structure diagrams were drawn based on the structure of each community, static life tables and survival curves were compiled, and the future development trends of each age group in… More >

Ugur Madran, Saeed Mian Qaisar^*, Duygu Soyoglu

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1969-1992, 2025, DOI:10.32604/cmes.2025.072024 - 26 November 2025

Abstract Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids. It offers substantial benefits across social, environmental, and economic dimensions. To effectively realize these advantages, a fine-grained collection and analysis of smart meter data is essential. However, the high dimensionality and volume of such time-series present significant challenges, including increased computational load, data transmission overhead, latency, and complexity in real-time analysis. This study proposes a novel, computationally efficient framework for feature extraction and selection tailored to smart meter time-series data. The approach begins with an extensive offline analysis, where features are… More >

Syeda Zoupash Zahra¹, Najia Saher², Malik Muhammad Saad Missen³, Rab Nawaz Bashir^4,5, Salma Idris⁵, Tahani Jaser Alahmadi^6,*, Muhammad Inshal Khan⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4869-4883, 2025, DOI:10.32604/cmc.2025.067639 - 23 October 2025

Abstract The increasing frequency and intensity of natural disasters necessitate advanced prediction techniques to mitigate potential damage. This study presents a comprehensive multi-hazard early warning framework by integrating the multi-source data fusion technique. A multi-source data extraction method was introduced by extracting pressure level and average precipitation data based on the hazard event from the Cooperative Open Online Landslide Repository (COOLR) dataset across multiple temporal intervals (12 h to 1 h prior to events). Feature engineering was performed using Choquet fuzzy integral-based importance scoring, which enables the model to account for interactions and uncertainty across multiple… More >

Seunghwan Seo^1,2,*, Moonkyung Chung¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2893-2922, 2025, DOI:10.32604/cmes.2025.069668 - 30 September 2025

Abstract Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within… More > Graphic Abstract

Muhammad Salman Qamar^1,2,*, Muhammad Fahad Munir², Athar Waseem²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3557-3584, 2025, DOI:10.32604/cmes.2025.067447 - 30 September 2025

Abstract Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is… More >

Gaoyong Lu¹, Yang Ou¹, Zhihong Wang², Yingnan Qu², Yingsheng Xia², Dibin Tang², Igor Kotenko³, Wei Li^2,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2403-2441, 2025, DOI:10.32604/cmc.2025.068024 - 23 September 2025

Abstract Deep learning (DL) has revolutionized time series forecasting (TSF), surpassing traditional statistical methods (e.g., ARIMA) and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data. This comprehensive survey reviews state-of-the-art DL architectures for TSF, focusing on four core paradigms: (1) Convolutional Neural Networks (CNNs), adept at extracting localized temporal features; (2) Recurrent Neural Networks (RNNs) and their advanced variants (LSTM, GRU), designed for sequential dependency modeling; (3) Graph Neural Networks (GNNs), specialized for forecasting structured relational data with spatial-temporal dependencies; and (4) Transformer-based models, leveraging self-attention mechanisms to… More >