Machine learning models for predicting spatiotemporal dynamics of groundwater recharge by Azeddine Elhassouny

“…A comparison of spatiotemporal prediction models' estimates of groundwater recharge in Morocco revealed AdaBoost and RF were the more accurate methods for temporal and spatial prediction, with RMSE values of 10.9712 mm/month and 5.0089 mm/month, respectively. …”
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Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration by Divas Karimanzira

“…In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. …”
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Traffic flow prediction based on spatiotemporal encoder-decoder model. by Yuanming Ding, Wei Zhao, Lin Song, Chen Jiang, Yunrui Tao

“…Specifically, on the PeMSD8 dataset, the model achieves reductions in MAE, RMSE, and SMAPE by 7.9%, 2.1%, and 16.9%, respectively, compared to the AMRGCN model for 1-hour predictions. …”
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Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit by Hongtai Yang, Chaojing Li, Xuan Li, Jinghai Huo, Yi Wen, Emma G. P. Sexton, Yugang Liu

“…Direct ridership models can predict station-level urban rail transit ridership. …”
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TGN: A Temporal Graph Network for Physics Prediction by Miaocong Yue, Huayong Liu, Xinghua Chang, Laiping Zhang, Tianyu Li

“…Long-term prediction of physical systems on irregular unstructured meshes is extremely challenging due to the spatial complexityof meshes and the dynamic changes over time; namely, spatial dependence and temporal dependence. …”
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Complex multivariate model predictions for coral diversity with climatic change by Tim R. McClanahan, Maxwell K. Azali, Nyawira A. Muthiga, Sean N. Porter, Michael H. Schleyer, Mireille M. M. Guillaume

“…We examined the predictions for numbers of coral taxa using all variables and compared them to models based on variables commonly used to predict climate change and human influences (eight and nine variables). …”
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Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda by Jean Marie Vianney Ntamwiza, Hannibal Bwire

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Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

“…A range of data-driven models based on the representation learning of multiple data sources have focused on extracting spatially explicit characteristics at the feature level for urban function inference. …”
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Assessment of Machine Learning Models for Predicting Aboveground Biomass in the Indian Subcontinent by S. Mamgain, B. Ghale, H. C. Karnatak, A. Roy

“…The predictions reveal significant spatial variation in biomass density, reflecting region's diverse ecological zones & land-use patterns. …”
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Evaluation of the relationships between physico-chemical parameters and the abundance of Vibrio spp. in blue crabs (Callinectes sapidus) and seawater from the Maryland Coastal Bays by Jasmine Smalls, John Jacobs, Howard Townsend, Paulinus Chigbu, Salina Parveen

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Individual mobility prediction by considering current traveling features and historical activity chain by Xiaotong Zhang, Zhipeng Gui, Yuhang Liu, Dehua Peng, Qianxi Lan, Zhangxiao Shen, Huan Chen, Yuhui Zuo, Yao Yao, Huayi Wu, Kai Li, Kun Qin

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Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…The emergence of machine learning (ML) and, more specifically, deep learning (DL) has introduced new techniques that significantly enhance prediction accuracy. ML models, such as support vector machines and ensemble models, use tabular data points to identify patterns and predict fire behavior. …”
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Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles. by Alexandra V Kulinkina, Yvonne Walz, Magaly Koch, Nana-Kwadwo Biritwum, Jürg Utzinger, Elena N Naumova

“…We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches.<h4>Methodology</h4>Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). …”
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The Role of Landscape Metrics and Spatial Processes in Performance Evaluation of GEOMOD (Case Study: Neka River Basin) by Shrif Joorabian Shooshtari, Kamran Shayesteh, Mehdi Gholamalifard, Mahmood Azari, Juan Ignacio López-Moreno

“…The relative error obtained by comparison of observed map versus simulated map for patch density, related circumscribing circle, and for effective mesh size metrics was the highest. The model was able to predict shape complexity, fragmentation, compactness and spatial heterogeneity, and area of forest class with high consistency. …”
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Research on spatial prediction technology for mitigating tunnel inrush disasters under complex geological conditions in China’s Hengduan Mountain Range by Yang Zou, XiuJun Dong, Tao Feng, ZhengXuan Xu, Hailin He, ZhangLei Wu

“…This spatial prediction and analysis method is highly effective and has practical and promotional value.…”
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Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…The sampling strategy was designed to ensure comprehensive data collection, allowing for robust model training and validation. MaxEnt, which is a presence-only model, outperformed both the GARP and logistic regression models in predicting suitable habitats for Astragalus sp. …”
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Mathematical modeling links Wnt signaling to emergent patterns of metabolism in colon cancer by Mary Lee, George T Chen, Eric Puttock, Kehui Wang, Robert A Edwards, Marian L Waterman, John Lowengrub

“…Partial interference with Wnt alters the size and intensity of the spotted pattern in tumors and in the model. The model predicts that Wnt inhibition should trigger an increase in proteins that enhance the range of Wnt ligand diffusion. …”
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Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting by Na Hu, Dafang Zhang, Kun Xie, Wei Liang, Meng-Yen Hsieh

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Impacts of Climate Change on the Spatial Distribution and Habitat Suitability of <i>Nitraria tangutorum</i> by Lianxing Li, Zhiqing Jia, Lingxianzi He, Dong Han, Qiankun Yang, Jialuo Li, Pingyi Zhou

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Integration of fuzzy logic and geographic weighted regression modeling for enhanced groundwater potential mapping using remote sensing and GIS by Junguang Gao, Amnah A. Alasgah, Imran Ahmad, Faten Nahas, Mithas Ahmad Dar, Martina Zelenakova, Milashu Sisay, Getanew Sewnet Zewdu

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