Application of Machine Learning Methods for Gravity Anomaly Prediction by Katima Zhanakulova, Bakhberde Adebiyet, Elmira Orynbassarova, Ainur Yerzhankyzy, Khaini-Kamal Kassymkanova, Roza Abdykalykova, Maksat Zakariya

“…Results indicated that the Exponential GPR model demonstrated the highest predictive accuracy, outperforming other ML methods, with 72.9% of predictions having errors below 1 mGal. …”
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Unsupervised Action Anticipation Through Action Cluster Prediction by Jiuxu Chen, Nupur Thakur, Sachin Chhabra, Baoxin Li

“…These pseudo-labels are then input into a temporal sequence modeling module that learns to predict future actions in terms of pseudo-labels. …”
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Fully convolutional video prediction network for complex scenarios by Rui Han, Shuaiwei Liang, Fan Yang, Yong Yang, Chen Li

“…Traditional predictive models, often used in simpler settings, face issues like high latency and computational demands, especially in complex real-world environments. …”
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An approach for predicting landslide susceptibility and evaluating predisposing factors by Wanxin Guo, Jian Ye, Chengbing Liu, Yijie Lv, Qiuyu Zeng, Xin Huang

“…Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. …”
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Pedestrian Crossing Direction Prediction at Intersections for Pedestrian Safety by Younggun Kim, Mohamed Abdel-Aty, Keechoo Choi, Zubayer Islam, Dongdong Wang, Shaoyan Zhai

“…To address challenges posed by varying intersection geometries and camera perspectives, we developed a global coordinate system that standardizes spatial features. The framework leverages Transformer-based models, Graph Convolutional Networks (GCNs), and a hybrid Transformer+GCN approach to extract spatial and temporal features from the pedestrian behaviors. …”
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Housing Price Prediction - Machine Learning and Geostatistical Methods by Cellmer Radosław, Kobylińska Katarzyna

“…The study demonstrated that machine learning combined with geostatistical methods significantly improves the accuracy of housing price predictions. Local factors that influence housing prices can be directly incorporated into the model with the use of dedicated maps.…”
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Prediction of Chemical Gas Emissions Based on Ecological Environment by Guobin Chen, Shijin Li

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Predicting the spatio-temporal reproductive potential of Aedes aegypti by Mr Tarek Alrefae

“…This correlation necessitates an understanding of abundance dynamics and motivates spatio-temporal predictions. We extend a previously proposed theoretical model of mosquito reproductive potential, Index Q, which is a function of temperature, humidity, and precipitation (Lourenco 2017). …”
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Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization by Chuanwei Zhang, Dingshuai Liu, Paraskevas Tsangaratos, Ioanna Ilia, Sijin Ma, Wei Chen

“…A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. …”
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Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

“…This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. …”
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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

“…Separate models were developed for trajectory prediction and intention recognition, with the trajectory prediction outcomes subsequently applied to enhance the accuracy of intention recognition. …”
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Developing Transferable Fourier Transform Mid-Infrared Spectroscopy Predictive Models for Buffalo Milk: A Spatio-Temporal Application Strategy Analysis Across Dairy Farms by Han Jiang, Peipei Wen, Yikai Fan, Yi Zhang, Chunfang Li, Chu Chu, Haitong Wang, Yue Zheng, Chendong Yang, Guie Jiang, Jianming Li, Junqing Ni, Shujun Zhang

“…Moreover, when using the two application strategies that predicted contemporaneous samples as the model, and adding 30–70% of the samples from the predicted farm, the model application effect can be improved before the robust model has been fully developed.…”
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Text Geolocation Prediction via Self-Supervised Learning by Yuxing Wu, Zhuang Zeng, Kaiyue Liu, Zhouzheng Xu, Yaqin Ye, Shunping Zhou, Huangbao Yao, Shengwen Li

“…As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labeled samples, namely GeoSG (Geographic Self-Supervised Geolocation) model, which leverages self-supervised learning to improve text geolocation prediction in situations where labeled samples are unavailable. …”
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Spatial autocorrelation in machine learning for modelling soil organic carbon by Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa

“…This study compares various methods to account for spatial autocorrelation when predicting soil organic carbon (SOC) using random forest models. …”
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SITA: Predicting site-specific immunogenicity for therapeutic antibodies by Yewei Cun, Hao Ding, Tiantian Mao, Yuan Wang, Caicui Wang, Jiajun Li, Zihao Li, Mengdie Hu, Zhiwei Cao, Tianyi Qiu

“…This study introduces Site-specific Immunogenicity for Therapeutic Antibody (SITA), a novel computational framework that predicts B-cell immunogenicity score for not only the overall antibody, but also individual residues, based on a comprehensive set of amino acid descriptors characterizing physicochemical and spatial features for antibody structures. …”
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Urban Expansion Scenario Prediction Model: Combining Multi-Source Big Data, a Graph Attention Network, a Vector Cellular Automata, and an Agent-Based Model by Yunqi Gao, Dongya Liu, Xinqi Zheng, Xiaoli Wang, Gang Ai

“…Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. …”
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Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach by Alireza Zhalehdoost, Mohammad Taleai

“…In the spatial phase, emission inventory data are aggregated at three spatial resolutions (500 m, 750 m, and 1000 m) to evaluate their effect on model performance in predicting PM and NOx concentrations. …”
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Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality by Mu Yue, Jingxin Xi

“…In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. …”
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An integrated method of selecting environmental covariates for predictive soil depth mapping by Yuan-yuan LU, Feng LIU, Yu-guo ZHAO, Xiao-dong SONG, Gan-lin ZHANG

“…Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. …”
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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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