Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

“…The SARIMA model is based on time series prediction theory and performs well in some scenarios, but has limitations in dealing with non-stationary data and spatial heterogeneity. …”
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A Spatiotemporal-Adaptive-Network-Based Method for Predicting Axial Forces in Assembly Steel Struts with Servo System of Foundation Pits by Weiwei Liu, Jianchao Sheng, Jian Zhou, Jinbo Fu, Wangjing Yao, Kuan Chang, Zhe Wang

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Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter? by Anna Carter, Michael Kearney, Nicola Mitchell, Stephen Hartley, Warren Porter, Nicola Nelson

“…<div class="WordSection1"><p>The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. …”
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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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New approach to predict wastewater quality for irrigation utilizing integrated indexical approaches and hyperspectral reflectance measurements supported with multivariate analysis by Mohamed Gad, Reda Abd El Hamed, Ezzat A. El Fadaly, Ibrahim E. Mousa, Aissam Gaagai, Hani Amir Aouissi, Mohamed Hamdy Eid, Mostafa R. Abukhadra, Haifa A. Alqhtani, Ahmed A. Allam, Salah Elsayed

“…Similarly, the model provided accurate predictions for BOD, with R2 = 0.96 and 0.81 for calibration and validation. …”
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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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Research on the Evaluation and Spatial Distribution Optimization of the Field Intensity Effect of Rural Basic Education Infrastructure in Wuhan’s New Urban District: A Case Study o... by Liang Jiang, Jie Chen, Jing Luo, Guanghui Tian

Subjects: “…rural basic education infrastructure…”
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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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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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Investigating spatial skills and math anxiety as mediatorsinasequential mediation model: A pilot study by Lu Wang

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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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Modeling and mapping under-nutrition among under-five children in Ethiopia: a Bayesian spatial analysis by Fekade Getabil Habtewold, Butte Gotu Arero

“…Spatial modeling revealed that maternal age, breastfeeding practices, access to clean water and sanitation facilities, cooking practices, maternal education, and wealth status significantly influence the number of under-nutrition cases among children under five in Ethiopia. …”
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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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Mapping and Spatial Analysis of the Development of the Administrative Borders of Stavropol Region in 1785–2021 by Igor Yurievich Katorgin, Alexander Nikolaevich Roman

“…Modern methods of processing and analyzing spatial data have made it possible to carry out spatial coordination and mapping of the borders of the ATD displayed on ancient maps. …”
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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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