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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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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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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Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study by Tingting Song, Boyang Zang, Chui Kong, Xifang Zhang, Huihui Luo, Wenbin Wei, Zheqing Li

“…Therefore, it is crucial to develop automated and efficient methods for predicting therapeutic outcomes.MethodsWe have developed a predictive model for the surgical efficacy in ME patients based on deep learning and optical coherence tomography (OCT) imaging, aimed at predicting the treatment outcomes at different time points. …”
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Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model by Yunqi Gao, Dongya Liu, Xinqi Zheng, Xiaoli Wang, Gang Ai

“…Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. …”
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Multi-Scenario Simulation of Ecosystem Service Value in Xiangjiang River Basin, China, Based on the PLUS Model by Lisha Tang, Jingzhi Li, Chenmei Xie, Miao Wang

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A statistical framework for modelling migration corridors by Tristan A. Nuñez, Mark A. Hurley, Tabitha A. Graves, Anna C. Ortega, Hall Sawyer, Julien Fattebert, Jerod A. Merkle, Matthew J. Kauffman

“…We developed a novel statistical corridor modelling approach that predicts movement corridors from cost‐distance models fit directly to migration tracking data. …”
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adm: An R package for constructing abundance‐based species distribution models by Admir Cesar deOliveira Junior, Santiago José Elías Velazco

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Research on Defect Detection for Overhead Transmission Lines Based on the ABG-YOLOv8n Model by Yang Yu, Hongfang Lv, Wei Chen, Yi Wang

“…The model incorporates four key improvements: Lightweight convolutional neural networks and spatial–channel reconstructed convolutional modules are integrated into the backbone network and feature fusion network, respectively. …”
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A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau by Ritu Wu, Zhimin Hong, Wala Du, Yu Shan, Hong Ying, Rihan Wu, Byambakhuu Gantumur

“…The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. …”
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Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target by Gu-Wei Ji, Zheng-Gang Xu, Shuo-Chen Liu, Shu-Ya Cao, Chen-Yu Jiao, Ming Lu, Biao Zhang, Yue Yang, Qing Xu, Xiao-Feng Wu, Ke Wang, Yong-Xiang Xia, Xiang-Cheng Li, Xue-Hao Wang

“…We aimed to unveil a novel radiotranscriptomic signature that can facilitate treatment response prediction by multi-omics integration and multiscale modelling. …”
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Simulation analysis on the evolution driven model of global copper ore trade network by Qishen Chen, Kun Wang, Yanfei Zhang, Qing Guan, Jiayun Xing, Tao Long, Guodong Zheng, Qiang Li, Zhenqing Li, Xin Ren, Chenghong Shang, Yueran Duan

“…The global copper ore trade network is influenced by various factors, including resource distribution, supply, demand, prices, transportation costs, etc.MethodsTo understand the evolution process of copper trade network and to predict the trend of supply chain structure evolution in future, in this paper, we construct a spatial weighted complex network evolution model based on complex network theory and gravity model using the import and export data and distance data of countries from 1990 to 2022.Results and discussionSimulation results show that the possibility of establishing copper ore trade between countries follows the spatial weighted complex network evolution model. …”
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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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Data quality and uncertainty issues in flood prediction: a systematic review by Jinhui Yu, Yichen Li, Xiao Huang, Xinyue Ye

“…These datasets often suffer from issues such as incompleteness, inconsistency, and accuracy deficits, further complicated by uncertainties arising from complex spatial features and environmental changes. The literature proposes a range of solutions, including the development of innovative methodologies, model construction, and comparative analysis, to address these challenges. …”
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On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

“…Recent geospatial machine learning studies have shown that the results of model evaluation via cross-validation (CV) are strongly affected by the dissimilarity between the sample data and the prediction locations. …”
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Developing Transferable Fourier Transform Mid-Infrared Spectroscopy Predictive Models for Buffalo Milk: A Spatio-Temporal Application Strategy Analysis Across Dairy Farms by Han Jiang, Peipei Wen, Yikai Fan, Yi Zhang, Chunfang Li, Chu Chu, Haitong Wang, Yue Zheng, Chendong Yang, Guie Jiang, Jianming Li, Junqing Ni, Shujun Zhang

“…Moreover, when using the two application strategies that predicted contemporaneous samples as the model, and adding 30–70% of the samples from the predicted farm, the model application effect can be improved before the robust model has been fully developed.…”
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Suitability prediction of potential arable land in southeast coastal area of China by Yan Zheng, Xiaohuang Liu, Jianwei Shi, Ping Zhu, Run Liu, Liyuan Xing, Hongyu Li, Chao Wang

“…Then, the spatial distribution pattern and centroid migration trend of the potential habitat area under two greenhouse gas emission scenarios (SSP126 and SSP585) in the future 2021–2040 (2040s) and 2041–2060 (2060s) were modeled. …”
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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…To ensure the reliability of the model predictions, extensive field campaigns were conducted across representative Romanian forests. …”
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Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China by Ge Shi, Chuang Chen, Qingci Cao, Jingran Zhang, Jinghai Xu, Yu Chen, Yutong Wang, Jiahang Liu

“…This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. …”
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A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China by Rihui Yang, Yuqing Zhong, Xiaoxiang Zhang, Aizemaitijiang Maimaitituersun, Xiaohan Ju

“…In the validation of the correlation accuracy between the downscaling results and the measured groundwater levels, the Random Forest model demonstrated better predictive performance, which offers distinct advantages in improving the spatial resolution of groundwater storage changes in Jiangsu Province.…”
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