Study on Key Influencing Factors of Carbon Emissions from Farmland Resource Utilization in Northeast China Under the Background of Energy Conservation and Emission Reduction by Mulin Sun, Yuhao Fu, Mingyao Sun, Run Huang, Yun Teng

“…A gray prediction model is constructed to predict the carbon emissions from the utilization of farmland resources in the next 10 years, and the logarithmic mean Divisia index model is used to analyze the effects of the various influencing factors on the carbon emissions from farmland utilization. …”
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Modelling the Spatial Dependence of Multi‐Species Point Patterns by Chathuri L. Samarasekara, Ian Flint, Yan Wang

“…ABSTRACT The study of the spatial point patterns in ecology, such as the records of the observed locations of trees, shrubs, nests, burrows, or documented animal presence, relies on multivariate point process models. …”
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Data Fusion and Dimensionality Reduction for Pest Management in Pitahaya Cultivation by Wilson Chango, Mónica Mazón-Fierro, Juan Erazo, Guido Mazón-Fierro, Santiago Logroño, Pedro Peñafiel, Jaime Sayago

“…First, early fusion better captures cross-domain interactions before dimensionality reduction, improving prediction robustness. Second, KPCA-poly offers an effective non-linear mapping suitable for tropical agroecosystem complexity. …”
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Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models by Eden T. Wasehun, Leila Hashemi Beni, Courtney A. Di Vittorio, Christopher M. Zarzar, Kyana R.L. Young

“…On the other hand, the SVR model demonstrated better predictive performance for Chl-a concentration retrieval using PlanetScope (PS) data (R2 = 0.71, RMSE = 8.15 μg/l, bias = 0.46). …”
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Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm by Sanglin Zhao, Zhetong Li, Hao Deng, Xing You, Jiaang Tong, Bingkun Yuan, Zihao Zeng

“…Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. …”
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Urban Fire Spatial–Temporal Prediction Based on Multi-Source Data Fusion by Haiyu Xiang, Lizhi Wu, Zidong Guo, Shaoyun Ren

“…Temporal variables, such as past fire incidents and external influences like meteorological conditions, significantly impact fire risk, while spatial attributes, including regional characteristics and cross-regional interactions, further complicate predictive modeling. …”
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Drone-assisted climate-smart agriculture (DACSA): A spatially-based outcome prediction model as an initial approach to track yield changes in shallot planting areas

“…Machine learning algorithms were employed to make predictions, and the yield projections were integrated into spatial maps. …”
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Vers une nouvelle classification des modèles d'évaluation et de prédiction de l'érosion hydrique by Aïcha Fadil, Farid El Wahidi

“…However, these models contrast considerably regarding to the studied phenomena, their nature and their complexity in the field, and to the implemented approach, through the variety and the quality of used data, the spatial and temporal scales of application and the information obtained as output, types and uncertainties.An attempt to classify water erosion assessment models (129 models are considered) is presented based on their most critical geospatial attributes for water erosion modeling. …”
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Pedestrian Trajectory Prediction via Window Attention and Spatial Graph Interaction Network by Xiang Gu, Chao Li, Jie Yang, Jing Wang, Qiwei Huang

“…However, the task still faces challenges in modeling long-term dependencies, complex spatial interactions, and multi-scale feature fusion. …”
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Spatial epidemiology of Tabanus (Diptera: Tabanidae) vectors of Trypanosoma by Roberta Marques, Daniel Jiménez-García, Luis E. Escobar, Tiago Kütter Krolow, Rodrigo Ferreira Krüger

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mNSF: multi-sample non-negative spatial factorization by Yi Wang, Kyla Woyshner, Chaichontat Sriworarat, Genevieve Stein-O’Brien, Loyal A. Goff, Kasper D. Hansen

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A novel surrogate model with deep learning for predicting spacial-temporal pressure in coalbed methane reservoirs by Yukun Dong, Xiaodong Zhang, Jiyuan Zhang, Kuankuan Wu, Shuaiwei Liu

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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

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Buzzing towards Resilience: Investigating the Spatial Alignment of the Desert Pallid Bee, <i>Centris pallida</i>, and Its Host Plants in Response to Climate Change by Terese Maxine P. Cruz, Stephen L. Buchmann, Kathleen L. Prudic

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Spatial interpolation of health and demographic variables: Predicting malaria indicators with and without covariates. by Camille Morlighem, Chibuzor Christopher Nnanatu, Corentin Visée, Atoumane Fall, Catherine Linard

“…Overall, socioeconomic indicators were generally better predicted by covariate-based models (e.g., random forest and Bayesian models), while methods using spatial autocorrelation alone (e.g., thin plate splines) performed better for variables with heterogeneous spatial structure, such as ethnicity and malaria prevention indicators. …”
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Graphormer Boosted: Molecular Property Prediction With Enhanced Graph Spatial and Edge Encodings by Sarah A. Fadlallah, Francesc Serratosa, Carme Julia

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MDA-MIM: a radar echo map prediction model integrating multi-scale feature fusion and dual attention mechanism by HU Qiang, GAO Yating, YIN Binli, QU Lianen

“…To better capture the non-stationary characteristics of radar echo data, a self-attention mechanism was introduced into the non-stationary module of the MIM model, dynamically adjusting the weights of different time steps and spatial positions. …”
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Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring by Fabiane Gallucci, Gustavo Fonseca, Danilo C Vieira, Luciana Erika Yaginuma, Paula Foltran Gheller, Simone Brito, Thais Navajas Corbisier

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Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring by Fabiane Gallucci, Gustavo Fonseca, Danilo C Vieira, Luciana Erika Yaginuma, Paula Foltran Gheller, Simone Brito, Thais Navajas Corbisier

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A Spatially Informed Machine Learning Method for Predicting Sound Field Uncertainty by Xiangmei Chen, Chao Li, Haibin Wang, Yupeng Tai, Jun Wang, Cyrille Migniot

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