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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Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data by DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim

“…Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R2 core of 0.981 on the test set. …”
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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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Prediction of difficulty in cryoballoon ablation with a three‐dimensional deep learning model using polygonal mesh representation by Kazutaka Nakasone, Makoto Nishimori, Masakazu Shinohara, Kunihiko Kiuchi, Mitsuru Takami, Kimitake Imamura, Yu Izawa, Toshihiro Nakamura, Yusuke Sonoda, Hiroyuki Takahara, Kyoko Yamamoto, Yuya Suzuki, Kenichi Tani, Hidehiro Iwai, Yusuke Nakanishi, Ken‐ichi Hirata, Koji Fukuzawa

“…This study aimed to develop a three‐dimensional (3D) deep learning (DL) model to predict CBA difficulty and compare its accuracy with conventional manual measurement. …”
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Spatial Autoregressive Modeling on Linear Mixed Models for Dependency Between Regions by Timbang Sirait

“…In this study, we are concerned with the spatial lag or SAR models because dependency between variables of interest is easier to predict. …”
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Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma by Jie Sun, Xuewei Wu, Xiao Zhang, Weiyuan Huang, Xi Zhong, Xueyan Li, Kaiming Xue, Shuyi Liu, Xianjie Chen, Wenzhu Li, Xin Liu, Hui Shen, Jingjing You, Wenle He, Zhe Jin, Lijuan Yu, Yuange Li, Shuixing Zhang, Bin Zhang

“…We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. …”
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A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios by Tao Jin, Shiyao Liu, Baorong Yan, Wei Guo, Changjiang Huang, Yu Hua, Shougang Zhang, Xiaohui Li, Lu Xu

“…A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. …”
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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. …”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

“…To improve the prediction accuracy of multi-region power load, an ultra-short-term multi-region power load forecasting model based on Spearman-GCN-GRU is proposed with focus on the spatial-temporal correlation analysis of multi-region power data. …”
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Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features by Abdullah Baktash, Yashar Sarbaz, Saeed Meshgini, Reza Afrouzian

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A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems by Tsikai S. Chinembiri, Onisimo Mutanga, Timothy Dube

“…Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. …”
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Sensitivity Analysis of Soil Hydraulic Parameters for Improved Flow Predictions in an Atlantic Forest Watershed Using the MOHID-Land Platform by Dhiego da Silva Sales, Jader Lugon Junior, David de Andrade Costa, Renata Silva Barreto Sales, Ramiro Joaquim Neves, Antonio José da Silva Neto

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A Predictive Compact Model of Effective Travel Time Considering the Implementation of First-Mile Autonomous Mini-Buses in Smart Suburbs by Andres Udal, Raivo Sell, Krister Kalda, Dago Antov

“…The one-dimensional distance-based spatial model with 5 residential origin zones and 6 destination districts in the city is applied. …”
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Spatial Simulation and Optimization of Cropping Structure Under Climate and Land Use Change Conditions Considering Synergistic Economic Benefits and Carbon Reduction in Crop Growth... by Mo Li, Haiyan Li, Zhaoqiang Zhou, Yingshan Chen, Yijia Wang, Tianxiao Li, Qiang Fu

“…This approach is based on climate change conditions and it accurately simulates future land use changes and crop growth processes, establishes a carbon emission intensity optimization model, and generates a spatial planting structure optimization and regulation scheme based on intelligent optimization algorithms under changing scenarios. …”
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Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals. by Reihaneh Hassanzadeh, Rogers F Silva, Anees Abrol, Mustafa Salman, Anna Bonkhoff, Yuhui Du, Zening Fu, Thomas DeRamus, Eswar Damaraju, Bradley Baker, Vince D Calhoun

“…The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. …”
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SHAP-enhanced interpretive MGTWR-CNN-BILSTM-AM framework for predicting surface subsidence: a case study of Shanghai municipality by Long Wen-Jiang, Yu Xue-Xiang, Zhu Ming-Fei, Xue Li, Zhang Guang-Hui, Wang Lin-Lin

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Development of Laser Underwater Transmission Model from Maximum Water Depth Perspective by Guoqing Zhou, Kun Li, Jian Gao, Junyun Ma, Ertao Gao, Yanling Lu, Jiasheng Xu, Xiao Zhou

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Generalizable Storm Surge Risk Modeling by Mahlon Scott, Hsin-Hsiung Huang

“…Inspired by principles of robust statistical modeling, this paper introduces a Bayesian hierarchical model integrated with Gaussian processes to account for spatial random effects. …”
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Optimizing the Portuguese wildfire fuel reduction program by Alan Ager, Bruno A. Aparício, José M.C. Pereira

“…In this study, we employed a scenario planning model to optimize the implementation of a national fuel management plan in Portugal and to understand tradeoffs among specific risk reduction objectives. …”
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