Actual and Predictive Transport Modeling of Fluoride Contamination of the Sfax-Agareb Coastal Aquifer in the Mediterranean Basin by Samira Melki, Amina Mabrouk El Asmi, Mohamadou Ould Baba Sy, Moncef Gueddari

“…This study, using numerical modeling, gives new insights for short- and medium-term prediction of eventual fluoride concentrations in the saturated zone of the Sfax-Agareb aquifer.…”
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A Digital Shadow for Modeling, Studying and Preventing Urban Crime by Juan Palma-Borda, Eduardo Guzman, Maria-Victoria Belmonte

“…Our approach transforms and integrates well-known criminological theories and the expert knowledge of law enforcement agencies (LEA), policy makers, and other stakeholders under a theoretical model, which is in turn combined with real crime, spatial (cartographic) and socio-economic data into an urban model characterizing the daily behavior of citizens. …”
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Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments by Megha Sharma, Shailendra Goel, Ani A. Elias

“…The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. …”
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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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A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling by Melissande Machefer, Michele Ronco, Anne-Claire Thomas, Michael Assouline, Melanie Rabier, Christina Corbane, Felix Rembold

“…The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises.…”
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Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak by Alvin Garcia, Sydney Liu, Xiaodi Du, Max Curie, Azarakhsh Jalalvand, Peter Steiner, Egemen Kolemen

“…This improvement highlights the model’s enhanced predictive accuracy for measured images from the validation set. …”
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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

“…A number of these aspects were evaluated using boosted regression tree models of numbers of coral taxa trained on ~1000 field surveys and 35 spatially complete influential environmental proxies at moderate scales (~6.25 km2). …”
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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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Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections by Md․ Ashraful Islam, Musabbir Ahmed Arrafi, Mehedi Hasan Peas, Tanvir Hossain, Md Mehedi Hasan, Sanzida Murshed, Monira Jahan Tania

“…The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. …”
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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

“…This study evaluates three machine learning models—Random Forest (RF), Gradient Tree Boosting (GTB), & Classification and Regression Trees (CART)—for predicting AGB across the subcontinent. …”
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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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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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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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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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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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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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IMPROVE OF UNCERTAIN MICROSATELLITE MAGNETIC CLEANLINESS BASED ON MAGNETIC FIELD SPATIAL HARMONICS COMPENSATION by Б.І. Кузнецов, Т.Б. Нікітіна, І.В. Бовдуй, К.В. Чуніхін, В.В. Коломієць, Б.Б. Кобилянський

“…Both vector game solution calculated based on particles multi-swarm optimization (PMSO) algorithms from Pareto optimal solutions taking into account binary preference relations. Prediction model and location of compensating units in spherical coordinates as well as multipole harmonic coefficients of dipoles, quadrupoles and octupoles are calculated during prediction and control of uncertain microsatellite MC. …”
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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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Collaborative Joint Perception and Prediction for Autonomous Driving by Shunli Ren, Siheng Chen, Wenjun Zhang

“…To address this challenge, we propose CoPnP, a novel collaborative joint perception and prediction system, whose core innovation is to realize multi-frame spatial–temporal information sharing. …”
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High-resolution spatial prediction of anemia risk among children aged 6 to 59 months in low- and middle-income countries by Johannes Seiler, Mattias Wetscher, Kenneth Harttgen, Jürg Utzinger, Nikolaus Umlauf

“…Methods Employing full probabilistic Bayesian distributional regression models, the research accurately predicts age-specific and spatially varying anemia risks. …”
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