Spatial assessment of current and future migration in response to climate risks in Ghana and Nigeria by Alina Schürmann, Mike Teucher, Janina Kleemann, Justice Nana Inkoom, Benjamin Kofi Nyarko, Appollonia Aimiosino Okhimamhe, Christopher Conrad

“…Migration has become a key adaptive response to these challenges, enabling communities to diversify livelihoods and enhance resilience. However, spatial patterns of migration in response to climate risks are not fully understood. …”
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Spatial and multilevel analysis of determinant factors for safely managed sanitation services in Ethiopia by Belay Desye, Abebe Kassa Geto, Chala Daba, Anmut Endalkachew Bezie, Semere Reda, Tadesse Sisay, Melaku Getachew, Leykun Berhanu

“…Addressing the sanitation problem requires a comprehensive understanding of the spatial variation and determinant factors. This study aims to estimate the prevalence of unimproved sanitation services, identify hotspot areas, predict high-risk zones, detect spatial cluster and determinant factors in Ethiopia. …”
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Short-term photovoltaic power forecasting based on a new hybrid deep learning model incorporating transfer learning strategy by Tiandong Ma, Feng Li, Renlong Gao, Siyu Hu, Wenwen Ma

“…In addition, the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model. …”
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Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

“…The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. …”
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A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature by C. T. Doherty, C. T. Doherty, W. Wang, H. Hashimoto, H. Hashimoto, I. G. Brosnan

“…Finally, we examine spatial correlation in predictions and errors using conditional Gaussian simulation to sample from the joint spatial predictive distribution. …”
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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. …”
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Pedestrian Movement Prediction and Pattern Analysis by V. Toshev, K. Karamitov, D. Petrova-Antonova, D. Petrova-Antonova

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Evaluating landslide susceptibility: the impact of resolution and hybrid integration approaches by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia

“…The present study investigates the effectiveness of various landslide susceptibility machine learning (ML) models at multiple spatial resolutions. Using various conditioning factors, including topography, hydrology, and human influences, the study analyzed the predictive power of single, integrated, and comparative ML models. …”
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Predicting Potentially Suitable Habitats and Analyzing the Distribution Patterns of the Rare and Endangered Genus <i>Syndiclis</i> Hook. f. (Lauraceae) in China by Lang Huang, Weihao Yao, Xu Xiao, Yang Zhang, Rui Chen, Yanbing Yang, Zhi Li

“…In this study, we employed the MaxEnt model, integrated with geographic information systems (ArcGIS), to predict the potential distribution of <i>Syndiclis</i> under current and future climate scenarios, identify dominant bioclimatic drivers, and assess temporal and spatial shifts in habitat patterns. …”
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RUL Prediction of DC Contactor Using CNN-LSTM With Channel Attention and Fusion of Dual Aggregated Features by Sai Wang, Yuanfeng Zhang, Hao Huang, Yun Shi, Jianfei Si

“…This model effectively addresses DC contactor life prediction challenges, offering a promising tool for improving maintenance strategies and operational reliability.…”
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Epidemiological characteristics and spatial clustering analysis of human brucellosis in Zibo City, Shandong Province, China, 2006–2024 by Rongtao Zhao, Ruixuan Sun, Feng Zhang

“…The incidence of brucellosis in 2025 was predicted using an ARIMA model.ResultsData on human brucellosis cases in Zibo City from 2006 to 2024 were obtained from the national infectious disease reporting information management system. …”
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A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain at... by Magboul M. Sulieman, Fuat Kaya, Abdullah S. Al-Farraj, Eric C. Brevik

“…The proposed methodology was effective for describing HMs spatial variability in the environments investigated. • The proposed method is a novel way to predict soil HMs and their spatial distribution over large areas. • Remote sensing/digital elevation models (DEMs)-derived ECOVs are useful for predicting and digitally mapping soil HMs, thus important for future environmental monitoring studies. • Explainable algorithms (i.e., RF and SMLR) are able to utilize ECOVs for HMs prediction and to establish background concentrations over large areas.Therefore, the combination of machine learning and RS/DEMs-based ECOVs is crucial to overcome the disadvantages of HMs determination via conventional methods.…”
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Estimation Model and Spatio-Temporal Analysis of Carbon Emissions from Energy Consumption with NPP-VIIRS-like Nighttime Light Images: A Case Study in the Pearl River Delta Urban Ag... by Mengru Song, Yanjun Wang, Yongshun Han, Yiye Ji

“…Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. …”
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A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks by Zhixin Xia, Zhangqi Zheng, Feiyang Wei, Yongshan Liu, Lu Yu

“…Therefore, to effectively utilize the information of the dynamic network and improve the prediction efficiency as well as the prediction accuracy, this paper proposes a spatio-temporal tensor graph neural network model, which learns graph structural features from both spatial and temporal aspects to capture the evolution of the dynamic network. …”
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Modelling of agricultural land dynamics using artificial neural networks and geospatial analysis in Melur Taluk, Tamil Nadu, India by Gowthaman M S, Murugasan R, Srinivasa Raju K, Makendran C

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Generating High Spatial and Temporal Surface Albedo with Multispectral-Wavemix and Temporal-Shift Heatmaps by Sagthitharan Karalasingham, Ravinesh C. Deo, Nawin Raj, David Casillas-Perez, Sancho Salcedo-Sanz

“…Model predictions were also contrasted against ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). …”
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A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms by Guodong Wu, Diangang Hu, Yongrui Zhang, Guangqing Bao, Ting He

“…The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.…”
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A Scalable Data-Driven Surrogate Model for 3D Dynamic Wind Farm Wake Prediction Using Physics-Inspired Neural Networks and Wind Box Decomposition by Qiuyu Lu, Yuqi Cao, Pingping Xie, Ying Chen, Yingming Lin

“…Results demonstrate that the proposed surrogate model accurately predicts the 3D dynamic wake evolution for single-turbine and multi-turbine configurations. …”
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Bayesian Spatial and Trend Analysis on Ozone Extreme Data in South Korea: 1991–2015 by Cheru Atsmegiorgis Kitabo

“…The dataset contains the ozone data from 29 representative air monitoring sites in South Korea collected from 1991 to 2015. Spatial generalized extreme value (GEV) using maximum likelihood estimation (MLE) and two max-stable and Bayesian kriging models are the statistical models used for analysis. …”
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Distribution and Abundance of Phlebotominae, Vectors of Leishmaniasis, in Argentina: Spatial and Temporal Analysis at Different Scales by María Gabriela Quintana, María Soledad Fernández, Oscar Daniel Salomón

“…In the macroscale (regional), captures of vectors and records of human cases allowed the construction of risk maps and predictive models of vector distribution. In conclusion, in order to obtain valid results transferrable to control programs from spatial studies, special attention should be paid in order to assure the consistency between the spatial scales of the hypotheses, data, and analytical tools of each experimental or descriptive design.…”
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