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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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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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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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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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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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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High-resolution soil temperature and soil moisture patterns in space, depth and time: An interpretable machine learning modelling approach by Maiken Baumberger, Bettina Haas, Sindhu Sivakumar, Marvin Ludwig, Nele Meyer, Hanna Meyer

“…We trained random forest models that were able to predict soil temperature with a mean absolute error of 0.93 °C and soil moisture with a mean absolute error of 4.64 % volumetric water content. …”
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Characteristics of Flux Footprint over Typical Underlying Surface of Qinghai-Xizang Plateau by Zixin WANG, Lei ZHONG, Yaoming MA, Yunfei FU

“…The heterogeneity of the underlying surface affects the accuracy and representativeness of the land-atmosphere flux observation.The study on the flux footprint distribution of complex underlying surface over Qinghai-Xizang Plateau （QXP） is of great significance to the observation and simulation of land-atmosphere interaction and its influence on weather and climate.Flux footprint analysis plays a pivotal role in investigating the spatial representativeness of flux observation information.The Flux Footprint Prediction （FFP） model represents a proficient methodology for computing the flux footprint.Based on the observation data from multiple research stations， including the Qomolangma Atmospheric and Environmental Observation and Research Station， the Ngari Desert Observation and Research Station， the Nam Co Monitoring and Research Station for Multisphere Interactions， the Muztagh Ata Westerly Observation and Research Station， the Southeast Tibet Observation and Research Station for the Alpine Environment in 2013， the FFP model was utilized to investigate the sensitivity of model parameters concerning flux footprint distribution.Additionally， the spatiotemporal characteristics and specific influencing factors of flux footprint distribution at different stations were discussed， thereby providing valuable insights for the erection of future observing stations.The results reveal that the primary determinants of flux footprint are measurement height， wind speed and wind direction.Characterized by an underlying surface of evergreen coniferous forest， flux footprint at Linzhi station exhibits greater sensitivity to measurement height and planetary boundary layer depth compared to the other stations.In the QXP， the spatial extent of the flux footprint derived from the ultrasonic anemometer measurements ranges from approximately 250 m to 500 m.Among the five stations， Qomo station exhibited the lowest frequency of stable stratification times during daytime， representing 15.69% of the daytime data points， whereas Ali station had the lowest occurrence of unstable stratification times during nighttime， comprising for 13.32% of the nighttime data points.At these five stations on the TP， the nocturnal flux footprints demonstrate greater width and extent compared to their daytime counterparts.In summer， due to the influence of monsoon， the axis of flux footprint tends to be more consistent.Lake-land breeze at Nam Co station is the main factor affecting flux footprint， whereas glacier wind at Qomo station is the dominant factor.Linzhi station possesses the smallest footprint due to the smallest mean wind speed， thus demonstrating the highest level of representativeness among these five stations.Lowering the height of observation instruments at Qomo and Nam Co stations could potentially enhance the representativeness of in situ measurements.…”
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Ecological and temporal drivers of human-gaur conflict in Tamil Nadu, India by Thekke Thumbath Shameer, Priyambada Routray, A. Udhayan, Rangaswamy Kanchana, Senbagapriya Sekar, Sivaranjani Shankar, Dhayanithi Vasanthakumari, Selvakumar Subramaniyam

“…This study offers critical insights into the spatial ecology of HGC and demonstrates the utility of predictive modeling for identifying high-risk areas, informing proactive mitigation strategies for conservation managers.…”
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