Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Gonghe Basin by Hong Jia, Siqi Yang, Lianyou Liu, Hang Li, Zeshi Li, Yixin Chen, Jifu Liu

“…Based on the land use data of the Gonghe Basin from 1990 to 2020, the InVEST model was applied to analyze the spatiotemporal changes in carbon storage, and the PLUS model was used to predict the changes in carbon storage under three different development scenarios in 2030. …”
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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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Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland by Zeinab Bandpey, Soroush Piri, Mehdi Shokouhian

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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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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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Modelling the Current and Future Pollutant Emission from Non-Road Machinery: A Case Study in Shanghai by Rui Chen, Xuerui Yang, Lei Xi, Huajun Zhen, Guangli Xiu

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Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag model with a Bayesian machine-learning model by Hasan Tahir, Frank Cobelens, Christina Mergenthaler, Mirjam I Bakker, Tanveer Ahmed, Jake D Mathewson, Daniella Brals, Abdullah Latif, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Zia Samad, Ente Rood

“…A predictive negative binomial regression (NBR) model was created, and the presence of spatial autocorrelation was examined to account for spatial dependencies in the outcome variable. …”
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