Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…The approach addresses key limitations in UBEM while offering a robust tool for policymakers and urban planners to optimize energy consumption and reduce carbon emissions. Integrating spatial and contextual factors with BER establishes a new standard for predictive accuracy in urban energy research.…”
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Spatiotemporal evolution and prediction of blue–green–grey-space carbon stocks in Henan Province, China by Kai Zhou, Kai Zhou, Xinyu Wei, Yanjie Wang, Jinhui Wang, Zhifang Wang, Zhifang Wang, Yichuan Zhang, Yichuan Zhang

“…Changes in blue–green–grey spaces use greatly influenced the carbon-storage capabilities of ecosystems, which is crucial for maintaining the carbon balance of regional ecosystems.By combining the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Patch-generating Land Use Simulation (PLUS) model, this study evaluates the spatiotemporal evolution of blue–green–grey spatial carbon stocks in Henan Province, China, and predicts the relationship between blue–green–grey spatial changes and carbon stocks under four future scenarios. …”
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Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang, Fan Wang

“…The 934 nm and 464 nm wavelengths were identified as the most critical spectral bands for predicting soil vanadium contamination. This integrated approach robustly delineates the spatial distribution characteristics of V and V5+ in soils, facilitating precise monitoring and ecological risk assessments of vanadium contamination through a comparative analysis of predictive accuracy across diverse models.…”
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Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features by Abdullah Baktash, Yashar Sarbaz, Saeed Meshgini, Reza Afrouzian

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Sensitivity Analysis of Soil Hydraulic Parameters for Improved Flow Predictions in an Atlantic Forest Watershed Using the MOHID-Land Platform by Dhiego da Silva Sales, Jader Lugon Junior, David de Andrade Costa, Renata Silva Barreto Sales, Ramiro Joaquim Neves, Antonio José da Silva Neto

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A cluster-based local modeling paradigm for high spatiotemporal resolution VPD prediction using multi-source data and machine learning by Mi Wang, Zhuowei Hu, Xiangping Liu, Wenxing Hou

“…The results show that the local modeling significantly enhances prediction accuracy, with the XGBoost model outperforming others across all clusters and maintaining high precision across seasons scales and different land use types. …”
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Skillful seasonal prediction of the boreal summer Pacific–Japan teleconnection pattern by Kan Yi, Chenqi Wang, Yunfei Zhang, Xiang Li, Jian Wang, Renqiang Wen, Mengjiao Du

“…Our findings elucidate that the spatial structure of the PJ pattern simulated by models introduces substantial diversities in prediction skills. …”
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L’action sur les territoires face au défi d’une temporalité ouverte. L’improvisation comme modèle pour l’action aménagiste ? by Lisa Levy

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L’action sur les territoires face au défi d’une temporalité ouverte. L’improvisation comme modèle pour l’action aménagiste ? by Lisa Levy

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Eco-Spatial Modeling of Two Giant Flying Squirrels (Sciuridae: <i>Petaurista</i>): Navigating Climate Resilience and Conservation Roadmap in the Eastern Himalaya and Indo-Burma Bio... by Imon Abedin, Manokaran Kamalakannan, Tanoy Mukherjee, Hilloljyoti Singha, Dhriti Banerjee, Hyun-Woo Kim, Shantanu Kundu

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Predicting species distributions in the open ocean with convolutional neural networks by Morand, Gaétan, Joly, Alexis, Rouyer, Tristan, Lorieul, Titouan, Barde, Julien

“…These findings show the adequacy of deep learning for species distribution modelling in the open ocean. Additionally, this purely correlative model was then analysed with explicability tools to understand which variables had an influence on the model’s predictions. …”
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The Influence of the Type of Educational Environment on Preschool Children's Mental Development by Shishova E.O.

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Spatial correlations and risk transmission of virtual water flow at city scale: A case study of the Yellow River Basin by Jingjing Yang, Zhong Ma, Weijing Ma, Xingxing Niu, Ting Mao

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Prediction of the change in suitable growth area of Sabina tibetica on the Qinghai-Tibetan plateau using MaxEnt model by Xiaoxiong Li, Dongsheng Yang, Jingjie Wang, Gang Pan

“…We employed the MaxEnt model with 10 bioclimatic and topographic variables to predict its distribution shifts under RCP4.5 and RCP8.5 scenarios for 2050 and 2070. …”
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A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes by Jiarui Yang, Kai Liu, Ming Wang, Gang Zhao, Wei Wu, Qingrui Yue

“…Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. …”
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Spatial pattern of the Chinese economy and spatial heterogeneity of its influential factors: An empirical study based on nighttime lighting data by Yifeng Hai, Yuanjie Deng, Hang Chen, Dingdi Jize, Ji Luo

“…Based on these, it uses a geographically weighted regression (GWR) model to explore the factors influencing the differences in China’s municipal economy and its spatial heterogeneity. …”
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Spatial disparities and determinants of modern contraceptive use among reproductive age women in Ethiopia: application of multilevel spatial analysis by Bisrategebriel Tesfaye Muchie, Ashenafi Abate Woya, Awoke Seyoum Tegegne, Maru Zewdu Kassie, Muluhabt Alene Assfaw, Wondaya Fenta Zewdia

“…The best-fitting model included individual, community, and spatial variables, with an intra-class correlation coefficient indicating that 15.57% of the variability in contraceptive use was due to cluster differences.ConclusionsModern contraceptive use among Ethiopian women is low and varies by cluster. …”
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Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand by Jarawadee Muenjak, Jutarat Thongrod, Chanakan Choodamdee, Pongphan Pongpanitanont, Manachai Yingklang, Tongjit Thanchomnang, Sakhone Laymanivong, Penchom Janwan

“…We developed an innovative predictive model by integrating convolutional neural networks (CNNs) for land-use classification of satellite imagery with artificial neural networks (ANNs) following dimensionality reduction through principal component analysis (PCA). …”
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Identification of the Physical Fabric Potential to Infill Development Applying Decision Making and GIS Models (case study: Ahvaz) by Ahmad Pourahmad, Saeid Zanganeh Shahraki, Mahmoud Arvin

“…The emergence of some problems due to physical-spatial growth of Ahvaz shifts the attention to critical approaches against horizontal distribution. …”
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Generation driven understanding of localized 3D scenes with 3D diffusion model by Hao Sun, Junping Qin, Zheng Liu, Xinglong Jia, Kai Yan, Lei Wang, Zhiqiang Liu, Shaofei Gong

“…In addition to accurately predicting the distribution of the noise tensor, the framework significantly enhances the understanding of localized scenes by effectively integrating spatial context information. …”
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