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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Bifurcation Branch in a Spatial Heterogeneous Predator–Prey Model with a Nonlinear Growth Rate for the Predator by Lei Kong

“…A strongly coupled predator–prey model in a spatially heterogeneous environment with a Holling type-II functional response and a nonlinear growth rate for the predator is considered. …”
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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…In order to better apply the stacking model to the prediction of new infectious diseases, we applied the prediction model based on the COVID-19 dataset to the prediction of the number of AIDS and pulmonary tuberculosis (PTB) cases, and verified the wide applicability of our model in the prediction of infectious diseases.…”
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Conditional logistic individual-level models of spatial infectious disease dynamics by Tahmina Akter, Rob Deardon

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Modeling Spatial and Temporal Changes in Land-Uses and Land Cover of the Urmia Lake Basin Applying Cellular Automata and Markov Chain by Hadi Eskandari Damaneh, Hamid Gholami, Hassan Khosravi, Rasoul Mahdavi Najafabadi, Asadollah Khoorani, Gimmy Li

“…After the integrated CA-Markov approach assessed the model, the land-use maps were predicted for the years 2028 and 2038. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Predictions used spatially exhaustive, auxiliary, and environmental covariables. …”
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Urban Signalized Intersection Traffic State Prediction: A Spatial–Temporal Graph Model Integrating the Cell Transmission Model and Transformer by Anran Li, Zhenlin Xu, Wenhao Li, Yanyan Chen, Yuyan Pan

“…In this framework, cells are modeled as nodes in a directed graph, with dynamic connections representing variations in signal phases, thereby embedding spatial relationships and signal information within dynamic graphs. …”
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Spatial Modeling of Auto Insurance Loss Metrics to Uncover Impact of COVID-19 Pandemic by Shengkun Xie, Jin Zhang

Subjects: “…spatial model…”
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The impact of digital economy on energy conservation and emission reduction: Evidence from prefecture-level cities in China by Cheng Zhang, Yirui Zhang, Hua Zhang, Jiaying You, Xinke Lv, Xinyu Cheng

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Predict the carcinogenicity of compounds with SGCN by Wei Ruobing, He Jiafeng, Qiu Xiaofang, Liu Qi

“…In this paper, 341 kinds of experimental data were obtained, and the spatial atom feature combined with the spatial graph convolutional network(SGCN) was used to establish a model that could predict the carcinogenicity of compounds. …”
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An optimized spatial target trajectory prediction model for multi-sensor data fusion in air traffic management by Jian Dong, Yuan Xu, Rigeng Wu, Chengwang Xiao

“…In comparative analyses, the proposed network significantly outperforms prevailing trajectory prediction models across multiple dimensions. In this paper, we propose a new deep learning network, and apply it to the real-world engineering challenge of spatial target trajectory prediction in the air traffic management domain.…”
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Vit-Traj: A Spatial–Temporal Coupling Vehicle Trajectory Prediction Model Based on Vision Transformer by Rongjun Cheng, Xudong An, Yuanzi Xu

“…In recent years, data-driven vehicle trajectory prediction models have become a significant research focus, and various spatial–temporal neural network models, based on spatial–temporal data, have been proposed. …”
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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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Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction by Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang

“…The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. …”
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Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan by Shoji Taniguchi, Takeshi Hayashi, Hiroshi Nakagawa, Kei Matsushita, Hiromi Kajiya-Kanegae, Jun-Ichi Yonemaru, Akitoshi Goto

“…To improve the prediction accuracy of models using historical data, we incorporated a spatial model to account for spatial structures among field stations, in addition to conventional genomic prediction models. …”
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Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm by Yubo Zhao, Mo Chen

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An AI-based gravitrap surveillance for spatial interaction analysis in predicting aedes risk by Hsiang-Yu Yuan, Pei-Sheng Lin, Wei-Liang Liu, Tzai-Hung Wen, Yu-Chun Lu, Chun-Hong Chen, Li‑Wei Chen

“…Information from neighboring villages is incorporated into the model to enhance precision of risk prediction. Results The proposed AI gravitrap index integrates the auto-Markov and disease mapping models to enhance sensitivity in risk prediction for Aedes densities. …”
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Prediction of the Spatial Distribution of Petrophysical Properties of Sediment Formations Using Multidimensional Splines by V. V. Lapkovsky, V. A. Kontorovich, K. I. Kanakova, S. E. Ponomareva, B. V. Lunev

“…A small number of direct measurements or their extremely uneven distribution leads to significant model errors. This article explores the possibility of using multidimensional approximation and regression splines, both considering spatially referenced direct observation data and using well log curves statistically linked to the modeled variables. …”
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SPATIAL MODEL OF BUILT-IN LAND CHANGE (NDBI) IN LANGSA CITY USING CELLULAR AUTOMATA MARKOV (CA-MARKOV) by Kania Maulia Rizky, Triyatno Triyatno

“…The method used in this study is a spatial-based quantitative method with the Cellular Automata Markov approach to create built-up land modelling in Langsa City, and the Analytical Hierarchy Process method to identify variables that influence changes in built-up land in Langsa City. …”
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