Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 by Chaibo Jose Armando, Joacim Rocklöv, Mohsin Sidat, Yesim Tozan, Alberto Francisco Mavume, Maquins Odhiambo Sewe

“…This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). …”
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A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction by Yikun Fan, Wei Zhang, Wenting Zhang, Dejin Zhang, Li He

“…This study introduces a novel double-layer long short-term memory (LSTM) model to surmount the limitations of conventional prediction methods, which frequently overlook predicted vehicle behavior and interactions. …”
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Global lightning-ignited wildfires prediction and climate change projections based on explainable machine learning models by Assaf Shmuel, Teddy Lazebnik, Oren Glickman, Eyal Heifetz, Colin Price

“…In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. …”
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A Spatiotemporal Convolutional Neural Network Model Based on Dual Attention Mechanism for Passenger Flow Prediction by Jinlong Li, Haoran Chen, Qiuzi Lu, Xi Wang, Haifeng Song, Lunming Qin

“…The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. …”
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Exploring malaria prediction models in Togo: a time series forecasting by health district and target group by Muriel Rabilloud, Nicolas Voirin, Anne Thomas, Tchaa Abalo Bakai, Tinah Atcha-Oubou, Tchassama Tchadjobo

“…Objectives Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. …”
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Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini, Ester Foppa Pedretti

“…This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. …”
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Cultivated Land Suitability Prediction in Southern Xinjiang Typical Areas Based on Optimized MaxEnt Model by Yilong Tian, Xiaohuang Liu, Hongyu Li, Run Liu, Ping Zhu, Chaozhu Li, Xinping Luo, Chao Wang, Honghui Zhao

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PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…Supervised principal component analysis and random survival forests were incorporated into the final model, which showed strong predictive ability in classifying patients. …”
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Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma by Juan I. Pisula, Doris Helbig, Lucas Sancéré, Oana-Diana Persa, Corinna Bürger, Anne Fröhlich, Carina Lorenz, Sandra Bingmann, Dennis Niebel, Konstantin Drexler, Jennifer Landsberg, Roman Thomas, Katarzyna Bozek, Johannes Brägelmann

“…Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored. …”
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High-resolution simulation and prediction of urban private vehicles energy consumption system: Agent-based modelling by Chuang Tu, Jiayi Liu, Jing Wang, Jing Bai, Guangwen Hu

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Uncertainties in predicting the medium-term future occurrence of grapevine budburst in Europe: the role of phenological models and climate scenarios by Sergi Costafreda-Aumedes, Luisa Leolini, Christoph Menz, Chenyao Yang, Giovanni Argenti, Camilla Dibari, Marco Bindi, Helder Fraga, Joao Santos, Daniel Molitor, J. Arturo Torres-Matallana, Marko Simeunović, Marco Moriondo

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Spatial–Temporal Difference of Urban Carbon Budget and Carbon Compensation Optimization Partition from the Perspective of Spatial Planning by Haifeng Yang, Guofang Zhai, Yifu Ge, Tong Jiang, Buda Su

“…These areas were further integrated with the “Three-zones and Three-lines” to reclassify nine spatial partition optimization types. Finally, we proposed a targeted emission reduction and sink enhancement optimization scheme. …”
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A Bayesian latent gaussian model with time-varying spatial weight matrices: Application to mod-eling the impact of multi-pollutant exposure on tuberculosis by I Gede Nyoman Mindra Jaya, Yudhie Andriyana, Bertho Tantular, Farah Kristiani

“… The main objective of spatiotemporal analysis is to offer precise predictions of outcomes. The objective of this study is to assess the accuracy of the Bayesian Latent Gaussian Model in predicting outcomes by utilizing both time-varying and fixed spatial weight matrices. …”
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Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma by Rong Su, Yong Du, Pan Tian, Weifang Ma, Yongfeng Hui, Shaoqi Yang

“…DiscussionOur study reveals that the RMRM risk model could effectively predict the prognosis of HCC, and SPP1+ macrophages regulated by miR-4739-RNA methylation promote the proliferation and migration of HCC cells. …”
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Spatial Variations of Physical Characteristics of Soil and Their Role in Creating a Model of a Geogenic Radon Hazard Index (GRHI) in the Kuznetsk Coal Basin by Timofey Leshukov, Konstantin Legoshchin, Maria Savkina, Elizaveta Baranova, Kirill Avdeev, Aleksey Larionov

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Spatiotemporal evolution and trend prediction of coupled coordination between digital technology and manufacturing green transformation from provinces in China by Xin Huang, Xin Huang, Hongbing Deng, Hongbing Deng

“…Based on this, this paper adopts the coupling coordination model, kernel density estimation, Dagum Gini coefficient decomposition, and spatial autocorrelation to conduct a spatiotemporal evolution analysis of the coupling coordination degree (the D‐G system) of digital technology and MGT in 30 provinces (municipalities, autonomous regions) of mainland China from 2011 to 2020, and adopting the spatial Markov chain to predict its evolutionary trend. …”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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Enhancing Landslide Susceptibility Mapping by Integrating Neighboring Information in Slope Units: A Spatial Logistic Regression by Leilei Li, Mingzhen Jia, Chong Xu, Yingying Tian, Siyuan Ma, Jintao Yang

“…In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. …”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM_2.5 levels, offering a broader spatial coverage and higher resolution. Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. …”
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Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods by Joohyung Roh, Sehong Min, Minsuk Kong

“…For comparative analysis, the YOLO segmentation model was used. Furthermore, the fire diameter and flame height were determined from the spatial information of the segmented flame, and the HRR was predicted based on the correlation between flame size and HRR. …”
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