Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area by G.A. Catalano, P.R. D'Urso, C. Arcidiacono

“…The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. …”
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Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems by Hui Wu, Zhijun Jin, Su Jiang, Hewei Tang, Joseph P. Morris, Jinjiang Zhang, Bo Zhang

“…Abstract A major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. …”
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Mechanical prediction method of strata movement and surface subsidence in backfill-strip mining by Xiaojun Zhu, Feng Zha, Guangli Guo, Qingliang Chang, Hui Liu, Xiaoyu Yang

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Evaluation of Statistical Models of NDVI and Agronomic Variables in a Protected Agriculture System by Edgar Vladimir Gutiérrez-Castorena, Joseph Alejandro Silva-Núñez, Francia Deyanira Gaytán-Martínez, Vicente Vidal Encinia-Uribe, Gustavo Andrés Ramírez-Gómez, Emilio Olivares-Sáenz

“…This has created a database to generate predictive models of development and yield as a function of nutrient status. …”
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Numerical Modeling of PMSM Noise Reduction by Harmonic Current Injection by Federico Soresini, Dario Barri, Federico Ballo, Massimiliano Gobbi

“…This order turns out to be dominated by a spatial 0 mode also referred to as the breathing mode. …”
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Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target by Gu-Wei Ji, Zheng-Gang Xu, Shuo-Chen Liu, Shu-Ya Cao, Chen-Yu Jiao, Ming Lu, Biao Zhang, Yue Yang, Qing Xu, Xiao-Feng Wu, Ke Wang, Yong-Xiang Xia, Xiang-Cheng Li, Xue-Hao Wang

“…We aimed to unveil a novel radiotranscriptomic signature that can facilitate treatment response prediction by multi-omics integration and multiscale modelling. …”
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Data quality and uncertainty issues in flood prediction: a systematic review by Jinhui Yu, Yichen Li, Xiao Huang, Xinyue Ye

“…These datasets often suffer from issues such as incompleteness, inconsistency, and accuracy deficits, further complicated by uncertainties arising from complex spatial features and environmental changes. The literature proposes a range of solutions, including the development of innovative methodologies, model construction, and comparative analysis, to address these challenges. …”
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On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

“…Recent geospatial machine learning studies have shown that the results of model evaluation via cross-validation (CV) are strongly affected by the dissimilarity between the sample data and the prediction locations. …”
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Suitability prediction of potential arable land in southeast coastal area of China by Yan Zheng, Xiaohuang Liu, Jianwei Shi, Ping Zhu, Run Liu, Liyuan Xing, Hongyu Li, Chao Wang

“…To ensure food security in the southeast coastal region, it is necessary to focus on the cultivated land degradation caused by climate change. This study predicted the potential suitable areas for cultivated land in the southeast coastal region using 32 environmental variables by the R-optimized MaxEnt model. …”
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STFGCN: Spatio-Temporal Fusion Graph Convolutional Networks for Subway Traffic Prediction by Xiaoxi Zhang, Zhanwei Tian, Yan Shi, Qingwen Guan, Yan Lu, Yujie Pan

“…Experimental results on the Hangzhou Metro’s inbound and outbound passenger flow datasets demonstrate that the STFGCN model exhibits significant superiority over baseline models and shows excellent performance in metro passenger flow prediction. …”
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An epidemical model with nonlocal spatial infections by Su Yang, Weiqi Chu, Panayotis Kevrekidis

“…Generalizing this ordinary differential equation (ODE) framework into a spatially distributed partial differential equation (PDE) model is a considerable challenge. …”
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Unveiling Hidden Dynamics in Air Traffic Networks: An Additional-Symmetry-Inspired Framework for Flight Delay Prediction by Chao Yin, Xinke Du, Jianyu Duan, Qiang Tang, Li Shen

“…To address this challenge, this study proposes a novel hybrid predictive framework named DenseNet-LSTM-FBLS. The framework first employs a DenseNet-LSTM module for deep spatio-temporal feature extraction, where DenseNet captures the intricate spatial correlations between airports, and LSTM models the temporal evolution of delays and meteorological conditions. …”
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Reconstructed hyperspectral imaging for in-situ nutrient prediction in pine needles by Yuanhang Li, Yuanhang Li, Jun Du, Jun Du, Chuangjie Zeng, Chuangjie Zeng, Yongshan Wu, Yongshan Wu, Junxian Chen, Junxian Chen, Teng Long, Teng Long, Yongbing Long, Yongbing Long, Yubin Lan, Yubin Lan, Xiaoliang Che, Tianyi Liu, Jing Zhao, Jing Zhao

“…However, its high cost and complexity hinder practical field applications.MethodsTo overcome these limitations, we propose a deep-learning-based method to reconstruct hyperspectral images from RGB inputs for in situ needle nutrient prediction. The model reconstructs hyperspectral images with a spectral range of 400–1000 nm (3.4 nm resolution) and spatial resolution of 768×768. …”
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Quantifying 3D coral reef structural complexity from 2D drone imagery using artificial intelligence by Aviv Suan, Simone Franceschini, Joushua Madin, Elizabeth Madin

“…The validation of our model resulted in R2 values of 0.71, 0.65, and 0.56 for each metric, respectively, indicating a robust predictive capability. …”
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The nexus between poverty reduction and carbon emissions: Insights from Hubei, China during the Targeted Poverty Alleviation Period by Mengxiao Liu, Yong Ge

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Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer by Yue Pan, Lei Fei, Shihua Wang, Hua Chen, Changqing Jiang, Hong Li, Changsong Wang, Yao Yang, Qinggao Zhang, Yongwen Chen

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Current and future climate suitability for the hazel dormouse in the UK and the impact on reintroduced populations by Emma L. Cartledge, Joe Bellis, Ian White, Jane L. Hurst, Paula Stockley, Sarah Dalrymple

“…We find no effect of climate suitability on adult dormouse counts at reintroduction sites, but dormouse counts decline with time since reintroduction. Future projections predict that climate change may lead to more widespread climate suitability for dormice in the UK, reflecting predicted changes in seasonality, winter temperature and precipitation. …”
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The Spatial Network Characteristics of the Coupling Coordination Degree Between Pollution and Carbon Reduction and High-Quality Development in the Yangtze River Economic Belt by Mengjiao Fan, Ying Wang, Sheng Ye, Chao Wei, Zhibin Xue

“…The coupling and coordination degree between pollution, carbon reduction, and high-quality development of 110 cities in the Yangtze River Economic Belt from 2010 to 2021 was measured, and the modified gravity model and social network analysis were then employed to assess the spatial network characteristics, patterns, and structure evolution. …”
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Improved digital mapping of soil texture using the kernel temperature–vegetation dryness index and adaptive boosting by Xu Zhai, Yuzhong Liu, Yuanyuan Hong, Yunjie Yang, Pengju Wang, Zhicheng Ye, Xiaoyan Liu, Tianlong She, Lihui Wang, Chen Xu, Lili Zhang, Qiang Wang

“…In this study, we collected 399 soil samples collected from Mingguang City in southeast China and made spatial predictions of soil texture based on remote sensing indices such as the kernel normalized difference vegetation index computed from Landsat8 data and topographic attributes computed via digital elevation model as environmental covariates. …”
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Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning by Xuemeng Tian, Sytze de Bruin, Rolf Simoes, Mustafa Serkan Isik, Robert Minarik, Yu-Feng Ho, Murat Şahin, Martin Herold, Davide Consoli, Tomislav Hengl

“…Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100–200 cm. …”
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