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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Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…Abstract Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. …”
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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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Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine by Penghai Zhang, Yang Li, Xin Dong, Tianhong Yang, Honglei Liu

“…The displacement time series were decomposed using Variational Mode Decomposition (VMD) into trend and periodic components, for which Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models were respectively developed. The results indicate that (1) DBSCAN effectively detects clusters characterized by high average cumulative displacement and broad spatial distribution, while filtering out isolated outliers. (2) The trend component prediction achieved a coefficient of determination (R²) of 0.99755, while the periodic component prediction yielded a root mean square error (RMSE) of just 0.0978 mm. …”
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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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Spectroscopic analysis (UV-VIS-NIR) for predictive modeling of macro and micronutrients in grapevine leaves by J.I. Manzano, M. Rodríguez-Febereiro, M. Fandiño, M. Vilanova, J.J. Cancela

“…The ultraviolet (UV) range played a minor role, highlighting the predominant importance of the VIS-NIR regions in spectroscopic analyses.Finally, the results support the potential of this technique for swiftly and non-invasively predicting both macro and micronutrient levels in grapevine plants, and facilitate the fertilization planning using variety-specific reference levels, or precision viticulture adapted to site-specific demands, including spatial intra-plot variability.…”
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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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Monitoring and forecasting of land use/land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Mod... by Ashraf Abdelkarim

“…This study sought to integrate the Cellular Automata-Markov Model (CA-Markov) and the Cellular Automata (CA) using sensing data for land cover maps for the years: 1988, 2000, 2013 and 2020 to monitor, detect, and predict the spatial and temporal of Land Use/Land Cover (LULC) change in Al-Hassa Oasis, Saudi Arabia. …”
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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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The spatial spillover effect of China's outgoing audit of natural resource assets on industrial pollution: Evidence from a spatial difference-in-differences method by Shasha Huang, Chang Luo, Zunhong Zhu

“…This study investigates the spatial spillover effects of the OANRA policy on industrial pollution reduction by developing spatial difference-in-differences (SDID) models. …”
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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…Extensive experiments conducted on the PeMSD3, PeMSD4, PeMSD7, and PeMSD8 datasets reveal the superiority of the proposed models, STGCN-EWC, STGCN-MAS, and STGCN-SI models achieve significant reductions in error rates compared to baseline methodologies. …”
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Criterial and level model of key universal educational activities formation in the process of teaching mathematics in a basic school by Maria Vasilyevna Demidova

“…A multidimensional spatial-level model is proposed, which vividly illustrates the dynamics of the formation of C.UUD from class to class. …”
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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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Deep Learning‐Guided Urban Climate Risk Mitigation Through Optimal Spatial Allocation of Green and Cool Roofs by JiHyun Kim, Suyeon Choi, Mahdi Panahi, Hocheol Seo, Yeonjoo Kim

“…Abstract With cities facing increasing challenges due to climate change, we developed a deep learning‐based surrogate modeling framework to optimize urban roofing strategies for climate risk mitigation. …”
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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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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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Learning behavior aware features across spaces for improved 3D human motion prediction by Ruiya Ji, Chengjie Lu, Zhao Huang, Jianqi Zhong

“…Additionally, we design an Euclidean Kinematic-Aware Extractor utilizing temporal-wise Kinematic-Aware Attention and spatial-wise Kinematic-Aware Feature Extraction. These two modules enhance and complement each other, leading to effective human motion prediction. …”
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Process model of development of adolescents’ social initiative in educational organization environment by Svyatoslav V. Beloklokov

“…The content block is aimed at the phased implementation of organizational forms and methods of the process model of the development of adolescents social initiative from the standpoint of using the capabilities of the educational organization environment (semantic, informational, activating) and its components (value-regulatory, informational, communicative, event, spatial). …”
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Effectiveness Model of Coastal Forest in Pananjung Nature Reserve, Pangandaran as Tsunami Buffer by Denni Susanto, Lies Rahayu Wijayanti Faida, Sunarto Sunarto

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