Reconstructed global monthly burned area maps from 1901 to 2020 by Z. Guo, W. Li, P. Ciais, S. Sitch, G. R. van der Werf, S. P. K. Bowring, A. Bastos, F. Mouillot, J. He, M. Sun, L. Zhu, X. Du, N. Wang, X. Huang

“…The trained models can well reproduce the long-term spatial patterns (slopes <span class="inline-formula">=</span> 0.70–1.28 and <span class="inline-formula"><i>R</i>²</span> <span class="inline-formula">=</span> 0.69–0.98 spatially), inter-annual variability and seasonality of the satellite-based burned area observations. …”
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Early signs of strong El Niño–Southern Oscillation episodes by Wenxiu Zhong, Arnold Sullivan, Gian Luca Eusebi Borzelli, Ziguang Li

“…AI technology has highlighted the importance of sea surface salinity in obtaining accurate ENSO predictions, even beyond the spring predictability barrier (SPB), which is commonly understood as a prediction barrier that occurs during the boreal spring. …”
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Regional Frequency Analysis Using L-Moments for Determining Daily Rainfall Probability Distribution Function and Estimating the Annual Wastewater Discharges by Pau Estrany-Planas, Pablo Blanco-Gómez, Juan I. Ortiz-Vallespí, Javier Orihuela-Martínez, Víctor Vilarrasa

“…The spatial distribution of precipitation is one of the major unknowns in hydrological modeling since meteorological stations do not adequately cover the territory, and their records are often short. …”
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ANALYSIS OF BATHYMETRIC DATA UTILIZATION FOR UNDERWATER MORPHOLOGY VISUALIZATION IN JEMAJA ANAMBAS ISLANDS RIAU ISLANDS INDONESIA by Bayu Dwi Santoso, Puspita Indra Wardhani, Ratih Puspita Dewi, Fitra Annurhutami

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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…With the rapid development of artificial intelligence and deep learning technology, a new opportunity is provided for physical education innovation. This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. …”
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Algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs by Binglin Liu, Weijia Zeng, Weijiang Liu, Yi Peng, Nini Yao

“…The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. …”
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Innovative Perspectives on Ecological Assessment in the Agro-Pastoral Ecotone of Inner Mongolia: An Integrated Evaluation and Forecast of Landscape and Ecological Risks and Drivers by Jiaru Wu, Peng Han, Jiwu Zhai, Qing Zhang

“…Future ecological risks from 2025 to 2040 were predicted using the multi-criteria evaluation-cellular automata-Markov model. …”
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Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila, Prakashini Koteshwar

“…Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. Conclusions: The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. …”
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Climate‐Associated Genetic Variation and Projected Genetic Offsets for Cryptomeria japonica D. Don Under Future Climate Scenarios by Kentaro Uchiyama, Tokuko Ujino‐Ihara, Katsuhiro Nakao, Jumpei Toriyama, Shoji Hashimoto, Yoshihiko Tsumura

“…ABSTRACT Revealing the spatial distribution of adaptive genetic variation is both a challenging and crucial task in evolutionary ecology, essential for understanding local adaptation within species, and in management, for predicting species responses to future climate change. …”
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Exploring the determinants of thyroid disorders in high-altitude western Himalaya: a geospatial and epidemiological study by Arshad Ahmed, Kheraj, Alireza Mohammadi, Robert Bergquist

“…Geographic information systems were utilized to map the spatial distribution of thyroid disorder prevalence, and the model’s predictive accuracy was validated using receiver operating characteristic (ROC). …”
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Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las... by Jingyi Liu, Yuxuan Cai, Xiwei Shen

“…Over the past two decades, research on residential segregation and environmental justice has evolved from spatial assimilation models to include class theory and social stratification. …”
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Synergistic Semantic Segmentation and Height Estimation for Monocular Remote Sensing Images via Cross-Task Interaction by Xuanang Peng, Shixin Wang, Futao Wang, Jinfeng Zhu, Suju Li, Longfei Liu, Zhenqing Wang

“…Recent studies have shown that multi-task learning methods can enhance the complementarity of task-related features, thus maximizing the prediction accuracy of multiple tasks at a low computational cost. …”
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Assimilation of Fengyun-4A Atmospheric Motion Vectors and Its Impact on China Meteorological Administration—Beijing System Forecasts by Yanhui Xie, Shuting Zhang, Xin Sun, Min Chen, Jiancheng Shi, Yu Xia, Ruixia Liu

“…The ever-increasing capacity of numerical weather prediction (NWP) models requires accurate flow information at higher spatial and temporal resolutions. …”
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Damage Assessment of Low-Velocity Impacted Sandwich Composite Structures Using X-Ray Micro-Computed Tomography by Tendai Chipanga, Ouassini Nemraoui, Fareed Ismail

“…These findings can be instrumental in developing a predictive impact damage model.…”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. …”
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Optimization Simulation and Comprehensive Evaluation Coupled with CNN-LSTM and PLUS for Multi-Scenario Land Use in Cultivated Land Reserve Resource Area by Shaner Li, Chao Zhang, Chang Chen, Cuicui Yang, Lihua Zhao, Xuechuan Bai

“…The SD scenario shows the optimal potential of combining carbon reduction and agricultural development. In this paper, deep learning and spatial modeling for multi-scenario simulation were integrated, and a scientific basis for the planning and management of cultivated land reserve resource areas was provided.…”
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Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation by Chenglin Yu, Lihong Shi, Yangyang Zhao

“…To address this, we propose a model that combines check-in trajectory information with user friendship relationships and uses a Transformer architecture for prediction (TraFriendFormer). …”
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Integrating Bayesian classification and ANN for lithofacies classification using well and seismic data: Bahregansar case study by Hessam Mansouri Siahgoli, Mohammad Ali Riahi, Majid Nabi-Bidhendi, Seyedmohsen Seyedali

“…Following well log corrections and shear wave velocity (Vs) prediction through rock physics modeling, an adaptive rock physics template (AI vs. …”
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Convolutional Neural Network-Based Risk Assessment of Regional Susceptibility to Road Collapse Disasters: A Case Study in Guangxi by Cheng Li, Zhixiang Lu, Yulong Hu, Ziqi Ding, Yuefeng Lu, Chuanzhi Han

“…This model predicts and maps the spatial distribution of collapse risk across Guangxi. …”
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Dunes under attack: untangling the effects of landscape changes on Iceplant invasion (Carpobrotus spp., Aizoaceae) in Mediterranean coasts by Flavio Marzialetti, Giacomo Grosso, Alicia Teresa Rosario Acosta, Marco Malavasi, Luigi Cao Pinna, Marcelo Sternberg, Sharad Kumar Gupta, Giuseppe Brundu, Maria Laura Carranza

“…We also analysed the relationship between Carpobrotus spp. patches and landscape pattern over time examining their respective temporal delta values, by Random Forest (RF) models followed by Partial Dependence analysis. The spatial-temporal characteristics of invaded patches and their contextual landscapes varied across coastal tracts experiencing IAP expansion or reduction. …”
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