Spatially explicit predictions using spatial eigenvector maps by Guillaume Guénard, Pierre Legendre

“…Abstract In this paper, we explain how to obtain sets of descriptors of the spatial variation, which we call “predictive Moran's eigenvector maps” (pMEM), that can be used to make spatially explicit predictions for any environmental variables, biotic or abiotic. …”
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Impact of data spatial resolution on barley yield prediction mapping by F. Ksantini, M. Quemada, N. Arencibia-Pérez, A.F. Almeida-Ñauñay, E. Sanz, Ana M. Tarquis

“…The choice of spatial resolution plays a key role in model performance, as the resolution of data input influences prediction quality. …”
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A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico by Manuel Solís-Navarro, Cruz Vargas-De-León, María Gúzman-Martínez, Josselin Corzo-Gómez

“…Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. …”
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Global Horizontal Irradiance Prediction Model Based on Mixed Spatial Information and Aerosol Classification by XiuYan Gao, YuTian Hou, Suning Li, Yuan Yuan

“…Finally, we used an aerosol classification model to classify aerosols in different regions and calculated the GHI predictions for different aerosol types. …”
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A Multifeatures Spatial-Temporal-Based Neural Network Model for Truck Flow Prediction by Shengyou Wang, Chunfu Shao, Yajiao Zhai, Song Xue, Yan Zheng

“…Therefore, in this paper, we focus on truck traffic flow and propose a Multifeatures Spatial-Temporal-Based Neural Network model (M-BiCNNGRU) to improve its prediction. …”
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Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models by Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

“…We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) introducing new predictors that represent spatial structure in the data, and (3) training our model on half the fires and predicting the other half of the fires. …”
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The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya. by Hugh J W Sturrock, Rachel L Pullan, Jimmy H Kihara, Charles Mwandawiro, Simon J Brooker

“…This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S. haematobium at every school along the Kenyan coast.…”
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Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis by Rui Huang, Miao Liu, Yongmei Ding

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Modelling and predicting biogeographical patterns in river networks by Sabela Lois

“…I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. …”
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Ephemeral gullies. A spatial and temporal analysis of their characteristics, importance and prediction by Jeroen Nachtergaele, Jean Poesen, Gerard Govers

“…This study, therefore, aimed at:1) describing spatial and temporal variations in ephemeral gully characteristics, in three contrasting environments;2) extending the existing studies on the importance of ephemeral gully erosion in space and time by using high-altitude stereo aerial photos (HASAP) to assess ephemeral gully volumes;3) improving ephemeral gully prediction, through the development of both empirical relationships to directly predict ephemeral gully volumes and process-oriented relationships to be built in physically-based erosion models;4) evaluating the medium to long-term evolution of an (ephemeral) gully.…”
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SWRVM: Sliding Window Recurrent Vision Mamba Model for Long-Term Spatial-Temporal Prediction by Ge Peng, Yujing Zhong

“…The quantitative and qualitative visualization results demonstrate the SWRVM model outperforms the state-of-the-arts (SOTA) models in multi-scale variations long-term spatial-temporal prediction tasks.…”
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A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data. by Panagiotis Pergantas, Andreas Tsatsaris, Chrisovalantis Malesios, Georgia Kriparakou, Nikolaos Demiris, Yiannis Tselentis

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Exploring spatial machine learning techniques for improving land surface temperature prediction by Arunab K.S., Mathew A.

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How spatial scales enhance prediction: an interpretable multi-scale framework for bike-sharing demand prediction by Jiasong Zhu, Jingbiao Chen, Mingxiao Li, Wei Tu

“…However, most studies focus only on one specific spatial scale, thus ignoring the inter-scale synergy improvement on prediction performance. …”
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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Qingfeng He

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Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model by Abdlwahd Jamel Alqdori Areej

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Modeling the Spatial Distribution of Wildfire Risk in Chile Under Current and Future Climate Scenarios by John Gajardo, Marco Yáñez, Robert Padilla, Sergio Espinoza, Marcos Carrasco-Benavides

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Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany by S. Kunz, A. Schulz, M. Wetzel, M. Nölscher, T. Chiaburu, F. Biessmann, S. Broda

“…Global ML architectures enable predictions across numerous monitoring wells concurrently using a single model, allowing predictions over a broad range of hydrogeological and meteorological conditions and simplifying model management. …”
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Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region by Temitope Seun Oluwadare, Marina Pannunzio Ribeiro, Dongmei Chen, Masoud Babadi Ataabadi, Saba Hosseini Tabesh, Abiodun Esau Daomi

Subjects: “…flood spatial modeling…”
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Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity by ZHANG Xingfu, JIANG Yuanjun, ABI Erdi

“…DEC-Based Clustering: The DEC model predicted higher landslide densities in high- and very-high-susceptibility zones by capturing spatial heterogeneity. …”
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