Spatial Variations of Physical Characteristics of Soil and Their Role in Creating a Model of a Geogenic Radon Hazard Index (GRHI) in the Kuznetsk Coal Basin by Timofey Leshukov, Konstantin Legoshchin, Maria Savkina, Elizaveta Baranova, Kirill Avdeev, Aleksey Larionov

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Spatiotemporal evolution and trend prediction of coupled coordination between digital technology and manufacturing green transformation from provinces in China by Xin Huang, Xin Huang, Hongbing Deng, Hongbing Deng

“…Based on this, this paper adopts the coupling coordination model, kernel density estimation, Dagum Gini coefficient decomposition, and spatial autocorrelation to conduct a spatiotemporal evolution analysis of the coupling coordination degree (the D‐G system) of digital technology and MGT in 30 provinces (municipalities, autonomous regions) of mainland China from 2011 to 2020, and adopting the spatial Markov chain to predict its evolutionary trend. …”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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Enhancing Landslide Susceptibility Mapping by Integrating Neighboring Information in Slope Units: A Spatial Logistic Regression by Leilei Li, Mingzhen Jia, Chong Xu, Yingying Tian, Siyuan Ma, Jintao Yang

“…In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. …”
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Performance Evaluation of Real-Time Image-Based Heat Release Rate Prediction Model Using Deep Learning and Image Processing Methods by Joohyung Roh, Sehong Min, Minsuk Kong

“…For comparative analysis, the YOLO segmentation model was used. Furthermore, the fire diameter and flame height were determined from the spatial information of the segmented flame, and the HRR was predicted based on the correlation between flame size and HRR. …”
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A digital twin model of urban utility tunnels and its application [version 1; peer review: 2 approved] by Wu Jiansong, Hu Yanzhu, Fan chen, Cai Jitao, Fu Ming, Wang Xin, Zou Xiaofu

“…Subsequently, a natural gas leakage prediction model is developed to enable the efficient prediction of the spatial and temporal distribution in the case of leakage. …”
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Identifying species traits that predict vulnerability to climate change by Damien A. Fordham

“…A powerful solution is to analyse the growing volume of biological data on changes in species ranges and abundances using process-explicit ecological models that run at fine temporal and spatial scales and across large geographical extents. …”
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A Predictive Compact Model of Effective Travel Time Considering the Implementation of First-Mile Autonomous Mini-Buses in Smart Suburbs by Andres Udal, Raivo Sell, Krister Kalda, Dago Antov

“…The one-dimensional distance-based spatial model with 5 residential origin zones and 6 destination districts in the city is applied. …”
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A Secure and Efficient Framework for Multimodal Prediction Tasks in Cloud Computing with Sliding-Window Attention Mechanisms by Weiyuan Cui, Qianye Lin, Jiaqi Shi, Xingyu Zhou, Zeyue Li, Haoyuan Zhan, Yihan Qin, Chunli Lv

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Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours by Muhammed Cavus, Huseyin Ayan, Dilum Dissanayake, Anurag Sharma, Sanchari Deb, Margaret Bell

“…Compared to the best-performing traditional model (Linear Regression, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.3520</mn></mrow></semantics></math></inline-formula>), HCB-Net improved predictive accuracy by 13.5% in terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>, and outperformed other deep learning models such as LSTM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mo>−</mo><mn>0.3756</mn></mrow></semantics></math></inline-formula>) and GRU (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mo>−</mo><mn>0.6276</mn></mrow></semantics></math></inline-formula>), which failed to capture spatial patterns effectively. …”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM_2.5 levels, offering a broader spatial coverage and higher resolution. Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. …”
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MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq by Nabaz R. Khwarahm

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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim

“…The hybrid model integrates CNNs for spatial feature extraction and LSTMs for temporal dependency modeling, enhanced by a custom activation function and loss function tailored for the problem’s characteristics. …”
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SHAP-enhanced interpretive MGTWR-CNN-BILSTM-AM framework for predicting surface subsidence: a case study of Shanghai municipality by Long Wen-Jiang, Yu Xue-Xiang, Zhu Ming-Fei, Xue Li, Zhang Guang-Hui, Wang Lin-Lin

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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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Prediction of the Morphological Characteristics of Asymmetric Thaw Plate of Qinghai–Tibet Highway Using Remote Sensing and Large-Scale Geological Survey Data by Jianbin Hao, Zhenyang Zhao, Jianbing Chen, Zhiyun Liu, Fuqing Cui, Xiaona Liu, Wenting Lu, Jine Liu

“…Through integrating remote sensing data and large-scale geological survey results with an earth–atmosphere coupled numerical model and a random forest (RF) prediction framework, we assessed the spatial distribution of thaw asymmetry along the permafrost section of the QTH. …”
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Predicting the current potential and future world wide distribution of the onion maggot, Delia antiqua using maximum entropy ecological niche modeling. by Shuoying Ning, Jiufeng Wei, Jinian Feng

“…Onion maggot, Delia antiqua, larvae are subterranean pests with limited mobility, that directly feed on bulbs of Allium sp. and render them completely unmarketable. Modeling the spatial distribution of such a widespread and damaging pest is crucial not only to identify current potentially suitable climactic areas but also to predict where the pest is likely to spread in the future so that appropriate monitoring and management programs can be developed. …”
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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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