A Bayesian Hierarchical Model for Estimation of Abundance and Spatial Density of Aedes aegypti. by Daniel A M Villela, Claudia T Codeço, Felipe Figueiredo, Gabriela A Garcia, Rafael Maciel-de-Freitas, Claudio J Struchiner

“…Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.…”
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Sentiment prediction based on analysis of customers assessments in food serving businesses by Zoltan Geler, Miloš Savić, Brankica Bratić, Vladimir Kurbalija, Mirjana Ivanović, Weihui Dai

“…The comparison of several regression models with regards to prediction of customer satisfaction of restaurant and food services is presented. …”
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Norway’s electric vehicle revolution: unveiling greenhouse gas emissions reductions and material use of passenger cars across space and time by Lola S A Rousseau, Jan Sandstad Næss, Marine Lhuillier, Romain G Billy, Peter Schön, Edgar G Hertwich

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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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Firefront Forecasting in Boreal Forests: Machine Learning Approach to Predict Wildfire Propagation by A.-M. Raita-Hakola, I. Pölönen

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Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City by D. Pavlov, D. Pavlov, L. L. Vitanova, D. Petrova-Antonova

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Network level spatial temporal traffic forecasting with Hierarchical-Attention-LSTM by Tianya Zhang

“…This paper leverages diverse traffic state datasets from the Caltrans Performance Measurement System (PeMS) hosted on the open benchmark and achieved promising performance compared to well-recognized spatial-temporal prediction models. Drawing inspiration from the success of hierarchical architectures in various Artificial Intelligence (AI) tasks, cell and hidden states were integrated from low-level to high-level Long Short-Term Memory (LSTM) networks with the attention pooling mechanism, similar to human perception systems. …”
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Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning by Mingyi Kuang, Fei Fu, Fangzhou Tian, Liwei Lin, Can Du, Yuesong Zhang

Subjects: “…metro station spatial growth prediction…”
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Using Upstream and Downstream Traffic Information for Short term Traffic Flow Prediction Based on LSTM Recurrent Neural Network by MAN Chun-tao, KANG Dan-qing

“…We employ the long/shortterm memory (LSTM) recurrent neural network to analyze the impact of various input settings on shortterm traffic flow prediction performance First, we compared the shortterm traffic flow prediction performance for different combinations of traffic flow, speed and occupancy data on the same vehicle detection station (VDS) The results show that the inclusion of occupancy/speed information may help to enhance the performance of the model as awhole In order to introduce spatial information into the model, we further include as inputs traffic variables from the upstream and/or downstream vehicle detector stations and test 16 different input combinations for traffic flow prediction The experimental results show that the inclusion of both upstream and downstream traffic information in the model is very useful for improving the accuracy of shortterm traffic flow prediction…”
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Unraveling environmental influences on the spatial and temporal dynamics of cyanobacterial blooms in Lake Erhai during its early stage of eutrophication by Shangbo Yang, Jianzhong Lu, Xiaoling Chen, Xuejiao Hou, Zushuai Wei, Jinru Wu

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Spatial modeling of two mosquito vectors of West Nile virus using integrated nested Laplace approximations by Kristin J. Bondo, Diego Montecino‐Latorre, Lisa Williams, Matt Helwig, Kenneth Duren, Michael L. Hutchinson, W. David Walter

“…We observed different spatial patterns of abundance in the predictive risk maps of each of the species. …”
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Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study by Giulia Pullano, Lucila Gisele Alvarez-Zuzek, Vittoria Colizza, Shweta Bansal

“…ObjectiveThis study aimed to address the questions that are critical for developing accurate transmission models, predicting the spatial propagation of disease across scales, and understanding the optimal geographical and temporal scale for the implementation of control policies. …”
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Spatial risk patches of the Indian crested porcupine crop damage in southeastern Iran by Kamran Almasieh, Alireza Mohammadi

“…Conservation areas covered about 8% of the predicted spatial risk patches and 2.4% of the hotspots of agricultural damage, respectively. …”
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The impact of China pilot carbon market policy on electricity carbon emissions by Zelong Zhang, Yanli Xiao, Kun Zhang, Mengyuan Tang, Ting Ma

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An action decoding framework combined with deep neural network for predicting the semantics of human actions in videos from evoked brain activities by Yuanyuan Zhang, Manli Tian, Baolin Liu

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Comprehensive prediction of potential spatiotemporal distribution patterns, priority planting regions, and introduction adaptability of Elymus sibiricus in the Chinese region by Huan-Huan Lu, Yu-Ying Zheng, Yong-Sen Qiu, Liu-Ban Tang, Yan-Cui Zhao, Wen-Gang Xie

“…In this study, the geographical distribution and environmental data of E. sibiricus in China were collected, and the potential spatiotemporal distribution pattern, planting pattern, and introduction adaptability of E. sibiricus were comprehensively predicted by using ensembled ecological niche model and Marxan model. …”
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Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective by Angel Triana, Alfonso Llanderal, Pedro García-Caparrós, Manuel Donoso, Rafael Jiménez-Lao, John Eloy Franco Rodríguez, María Teresa Lao

“…The objectives of this experiment were to (1) discern the spatial variability in climatic parameters within a greenhouse throughout different phenological stages of pepper cultivation and (2) develop an empirical model aimed at establishing predictive equations for temperature, relative humidity, vapor pressure deficit, and crop evapotranspiration (ETc) within the greenhouse considering the climatic parameters recorded on the outside. …”
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Prediction method of gas content in deep coal seams based on logging parameters: A case study of the Baijiahai region in the Junggar Basin by Yijie Wen, Shu Tao, Fan Yang, Yi Cui, Qinghe Jing, Jie Guo, Shida Chen, Bin Zhang, Jincheng Ye

“…Notably, the gas enrichment areas are predominantly distributed in well blocks adjacent to fault systems, such as wells C31 and BJ8, etc., which align with the favorable geological conditions for deep CBM accumulation in the Baijiahai region. These spatial distribution patterns not only corroborate existing geological insights but also further validate the reliability of the MAML model in predicting gas content within deep coal seams.…”
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GL-ST: A Data-Driven Prediction Model for Sea Surface Temperature in the Coastal Waters of China Based on Interactive Fusion of Global and Local Spatiotemporal Information by Ning Song, Jie Nie, Qi Wen, Yuchen Yuan, Xiong Liu, Jun Ma, Zhiqiang Wei

“…The spatiotemporal multimodal variations in sea surface temperature refer to its diverse changes across different temporal and spatial scales. Understanding and predicting these variations are crucial for climate research and marine ecosystem conservation. …”
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Taxi origin and destination demand prediction based on deep learning: a review by Dan Peng, Mingxia Huang, Zhibo Xing

“…These findings offer valuable insights for model selection in OD demand prediction. Finally, we provide public datasets and open-source code, along with suggestions for future research directions.…”
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