Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…Lightning is one of the most severe weather hazards that will cause significant economic, social and environmental damage each year. The prediction of a lightning is a very difficult task due to the spatial and temporal expansion of weather either physically or dynamically. …”
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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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Short-term displacement prediction for newly established monitoring slopes based on transfer learning by Yuan Tian, Yang-landuo Deng, Ming-zhi Zhang, Xiao Pang, Rui-ping Ma, Jian-xue Zhang

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Spatial Position Reasoning of Image Entities Based on Location Words by Xingguo Qin, Ya Zhou, Jun Li

“…The incorporation of spatial position terms into the model was observed to elevate the average predictive accuracy by approximately three percentage points.…”
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Predicting changes in maximum temperatures in the mid-future period in Sistan and Baluchestan under SSP scenarios by abdolreza kashki, ghorban jafari

“…IDW interpolation in GIS was used to map spatial temperature changes. Paired-sample t-tests evaluated differences between baseline and mid-future periods.Finding: The CanESM5 model performed best in predicting temperature changes. …”
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Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis by Jin-Xin Zheng, Hui‐Hui Zhu, Shang Xia, Men‐Bao Qian, Robert Bergquist, Hung Manh Nguyen, Xiao‐Nong Zhou

“…Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. …”
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The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction by Chenyue Zhang, Qinxin Wang, Yiyang Li, Anqi Teng, Gang Hu, Qiqige Wuyun, Wei Zheng

“…Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. …”
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A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…However, traditional models struggle to capture the complex spatial and temporal dependencies in irradiance data, limiting prediction accuracy under varying weather conditions. …”
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Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer by Qinqing Wu, Qinqing Wu, Heng Cao, Jiangdong Jin, Dongxu Ma, Yixiao Niu, Yixiao Niu, Yanping Yu, Yanping Yu, Xiang Wang, Yiqin Xia

“…The prognostic model constructed based on the gene expression profile of PRDX1-positive monocytes demonstrated high accuracy in predicting patient survival in both the training and validation cohorts. …”
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Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions by Zongyue Lu, Genyuan Liu, Zhihong Song, Kang Sun, Ming Li, Yansi Chen, Xidong Zhao, Wei Zhang

“…Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. …”
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Ongoing invasions by American bullfrogs and red-eared sliders in the Republic of Korea by Desiree Andersen, Amaël Borzée, Yikweon Jang

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Post-Disaster Recovery Effectiveness: Assessment and Prediction of Coordinated Development in the Wenchuan Earthquake-Stricken Areas by Liang Zhao, Chunmiao Zhang, Xia Zhou

“…By constructing a framework to assess post-disaster coordinated development, this study utilized the entropy weight method and mean-variance method for the comprehensive weighting of evaluation indicators. The gray system prediction model G(1,1) was used to forecast the coordinated development levels of the three cities from 2019 to 2025. …”
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Multispheroidal model of magnetic field of uncertain extended energy-saturated technical object by B. I. Kuznetsov, T. B. Nikitina, I. V. Bovdui, K. V. Chunikhin, V. V. Kolomiets, B. B. Kobylianskyi

“…Coordinates of the geometric location and magnitudes of spatial extended spheroidal harmonics of spheroidal sources of multispheroidal model of magnetic field calculated as magnetostatics geometric inverse problems solution in the form of nonlinear minimax optimization problem based on near field measurements for prediction far extended technical objects magnetic field magnitude. …”
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Global distribution prediction and ecological conservation of basking shark (Cetorhinus maximus) under integrated impacts by Runlong Sun, Kaiyu Liu, Wenhao Huang, Xiao Wang, Hongfei Zhuang, Zongling Wang, Zhaohui Zhang, Linlin Zhao

“…This study employs various environmental variables and distribution data to construct a global species distribution model for basking sharks, predicting their distribution patterns under current and future climate scenarios. …”
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A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

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Predicting the Potential Geographic Distribution of <i>Phytophthora cinnamomi</i> in China Using a MaxEnt-Based Ecological Niche Model by Xiaorui Zhang, Haiwen Wang, Tingting Dai

“…Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis to systematically predict the potential geographical distribution of <i>P. cinnamomi</i> under current (1970–2000) and future (2030S, 2050S, 2070S, 2090S) climate scenarios across three Shared Socioeconomic Pathways (SSPs). …”
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Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China by Chengyu Li, Qunwei Wang, Peng Zhou

“…This study establishes the improved super-efficiency slack-based measure (Super-SBM) model and long short-term memory (LSTM) network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. …”
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A data-driven reduced-order model for fast prediction of resonant acoustic flow under vertical vibration based on secondary decomposition by Yuqi Gao, Ning Ma, Shifu Zhu, Pengchao Zhang, Hongxing Liu, Zhongyuan Xie

“…The original dataset is derived from an experimentally validated computational fluid dynamics model. The flow field snapshots are decomposed into spatial modes and temporal coefficients using proper orthogonal decomposition. …”
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Rapid Simulation of Floods by Considering the Spatial and Temporal Characteristics of Inundation by Rukai Wang, Jijian Lian, Ximin Yuan, Fuchang Tian, Kuang Li, Zhanyou Liu

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Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and <i>K</i>-Means Clustering by Zhenzhu Meng, Yating Hu, Shunqiang Jiang, Sen Zheng, Jinxin Zhang, Zhenxia Yuan, Shaofeng Yao

“…Additionally, we employ a <i>k</i>-means clustering technique to account for both temporal and spatial variations in multi-point monitoring data, which improves the model’s ability to capture the relationships between monitoring points and increases prediction relevance. …”
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