19 Predicting daily PM2.5 in Mexico City: A hybrid spatiotemporal modeling approach by Mike He, Ellen Ren, Iván Gutiérrez-Avila, Itai Kloog

“…Objectives/Goals: In recent years, there has been growing interest in the development of air pollution prediction models, particularly in low- and middle-income countries that are disproportionately impacted by the effects of air pollution. …”
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Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease. by Julia Ledien, Zulma M Cucunubá, Gabriel Parra-Henao, Eliana Rodríguez-Monguí, Andrew P Dobson, Susana B Adamo, María-Gloria Basáñez, Pierre Nouvellet

“…Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. …”
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Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus by J. M. Manel K. Herath, Hemalika T. K. Abeyasundara, W. A. Priyanka P. De Silva, Thilini C. Weeraratne, S. H. P. Parakrama Karunaratne

“…Another prediction model was developed using OVI and RH with one month lag period (R2 (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528∗ Lag 1 month RH; RMSE = 2.01). …”
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Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, W. Kent Tobiska

“…The newly developed EXTEMPLAR‐ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. …”
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Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields by Qassim A. Talib Al-Shujairy, Suhad M. Al-Hedny, Mohammed A. Naser, Sadeq Muneer Shawkat, Ahmed Hatem Ali, Dinesh Panday

“…Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. …”
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Assessment of landscape diversity in Inner Mongolia and risk prediction using CNN-LSTM model by Yalei Yang, Hong Wang, Xiaobing Li, Tengfei Qu, Jingru Su, Dingsheng Luo, Yixiao He

“…A Potential-Connectedness-Resilience framework was used to assess landscape diversity risks from 2010 to 2020, with a Convolutional Neural Network combined with a Long Short-Term Memory (CNN-LSTM) model predicting future risks for 2025. Our findings indicate that landscape diversity in Inner Mongolia was favourable and stable condition during 2019–2021. …”
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An interpretable coupled model (SWAT-STFT) for multispatial-multistep evapotranspiration prediction in the river basin by Zhonghui Guo, Chang Feng, Liu Yang, Qing Liu

“…This integration of physics-based and data-driven modeling not only provides valuable insights into watershed ET modeling prediction and mechanistic understanding but also underscores the broader potential for application across global watersheds and related disciplines.…”
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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Accurate prediction of greenhouse temperatures is essential for developing effective environmental control strategies, as the precision of minimum temperature data acquisition significantly impacts the reliability of predictive models. …”
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Fast prediction of irradiation-induced cascade defects using denoising diffusion probabilistic model by Ruihao Liao, Ke Xu, Yifan Liu, Zibo Gao, Shuo Jin, Linyun Liang, Guang-Hong Lu

“…We propose a computational scheme that combines molecular dynamic (MD) simulations with a denoising diffusion probabilistic model (DDPM) to rapidly and accurately predict the spatial coordinates of point defects at any given primary knock atom (PKA) energy, ranging from 0 to 100.0 keV. …”
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A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

“…First, tumor regions are delineated across MRI modalities and then modeled using a translation-invariant Markov-Gibbs random field (MGRF) with analytical parameter estimation to capture modality-specific spatial appearance patterns correlated with NAC response. …”
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Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. …”
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Method for prediction magnetic silencing of uncertain energy-saturated extended technical objects in prolate spheroidal coordinate system by B. I. Kuznetsov, A. S. Kutsenko, T. B. Nikitina, I. V. Bovdui, K. V. Chunikhin, V. V. Kolomiets

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DINOV2-FCS: a model for fruit leaf disease classification and severity prediction by Chunhui Bai, Chunhui Bai, Chunhui Bai, Lilian Zhang, Lilian Zhang, Lilian Zhang, Lutao Gao, Lutao Gao, Lutao Gao, Lin Peng, Lin Peng, Lin Peng, Peishan Li, Peishan Li, Peishan Li, Linnan Yang, Linnan Yang, Linnan Yang

“…However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.MethodsIn light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. …”
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PVD-GSTPS: design of an efficient parallel vehicle detection based green signal time prediction system by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…These advancements are essential for effectively predicting vehicle Green Signal Time by considering accurate detection and tracking, Spatial Occupancy calculation, long-term dependencies, and non-linear relationships in traffic data. …”
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Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learning in an endeavor to augment the predictive competency. …”
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Modeling spatial interaction networks of the gut microbiota by Xiaocang Cao, Ang Dong, Guangbo Kang, Xiaoli Wang, Liyun Duan, Huixing Hou, Tianming Zhao, Shuang Wu, Xinjuan Liu, He Huang, Rongling Wu

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Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago by Yuxiao Fan, Xiaofeng Hu, Jinming Hu

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Development and Spatial External Validation of a Predictive Model of Survival Based on Random Survival Forest Analysis for People Living With HIV and AIDS After Highly Active Antiretroviral Therapy in China: Retrospective Cohort Study by Xiaoshan Li, Yanhui Li, Zhengping Zhu, Bingxin Tan, Xiaoyi Zhou, Hongjie Shi, Xin Li, Ping Zhu, Yuanyuan Xu

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Modeling the spatial distribution of anthrax in southern Kenya. by Fredrick Tom Otieno, John Gachohi, Peter Gikuma-Njuru, Patrick Kariuki, Harry Oyas, Samuel A Canfield, Jason K Blackburn, M Kariuki Njenga, Bernard Bett

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Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Based on Multi-Source Information by WANG Qiang, BI Yuhao, GAO Chao, SONG Duoyang

“…The proposed charging demand gravity model optimizes users' charging station selection behavior by integrating factors such as charging station size， electricity price， and user time cost， resulting in a more reasonable spatial and temporal distribution of the charging load[Conclusions] This study constructed a spatial and temporal distribution prediction model for electric vehicle charging loads by integrating information from multiple sources. …”
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