Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S. by Lifeng Yuan, Kenneth J Forshay

“…However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. …”
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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

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Precise Spatial Prediction of Rice Seedlings From Large-Scale Airborne Remote Sensing Data Using Optimized Li-YOLOv9 by Jayakrishnan Anandakrishnan, Arun Kumar Sangaiah, Hendri Darmawan, Nguyen Khanh Son, Yi-Bing Lin, Mohammed J. F. Alenazi

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Analyzing the determinant factors of spatial groundwater availability in the Akaki catchment, Central Ethiopia by Getamesay Nigussie, Mekuria Argaw, Dessie Nedaw, Tsegaye Tadesse, Andreas Hartmann

“…Hence, this study investigated the spatial availability of groundwater within the catchment by considering eight different factors. …”
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Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china by Qimeng Ren, Ming Sun

“…By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. …”
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Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.Methods Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. …”
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The spatial risk of cyclone wave damage across the Great Barrier Reef by Mandy W.M. Cheung, Milani Chaloupka, Peter J. Mumby, David P. Callaghan

“…We then applied a statistical model with likelihood inference to predict damage given cyclone strength and reef spatial arrangement, and calibrated the model using field observations from five cyclones. …”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

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MODIFIED WEIGHT MATRIX USING PRIM’S ALGORITHM IN MINIMUM SPANNING TREE (MST) APROACH FOR GSTAR(1;1) MODEL by Nur'ainul Miftahul Huda, Fransiskus Fran, Yundari Yundari, Lisa Fikadila, Fauziah Safitri

“…The existence of a weight matrix is one of the aspects that established this model. The matrix illustrates the spatial impact that occurs between locations. …”
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Impact of low carbon orientation on green finance in highly polluted areas based on STIRPAT spatial panel model by Yunyan Yang

“…This study aims to analyze the impact of low-carbon orientation on green finance, clarify the intrinsic relationship between the two, and provide strategic recommendations for the development of low-carbon emission reduction finance in high pollution areas. To achieve the research objectives, this study used a random effects regression model and a spatial panel data model to conduct in-depth analysis of the carbon emission index in high pollution areas. …”
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A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning by Gbenga Lawrence Alawode, Pere Joan Gelabert, Marcos Rodrigues

“…Effectively suppressing large wildfires requires a thorough understanding of containment opportunities across landscapes, to which empirical spatial modelling can contribute largely. The previous containment model in Catalonia failed to account for the crucial roles of weather conditions, lacked temporal prediction and could not forecast windows for containment opportunities, prompting this research. …”
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Topographic Position Index Predicts Within-Field Yield Variation in a Dryland Cereal Production System by Jacob A. Macdonald, David M. Barnard, Kyle R. Mankin, Grace L. Miner, Robert H. Erskine, David J. Poss, Sushant Mehan, Adam L. Mahood, Maysoon M. Mikha

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A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) by Emilda Indrisari, Hidayat Febiansyah, Bambang Adiwinoto

“…This study recommends future research to focus on integrating spatial-temporal data, implementing Explainable AI (XAI), and conducting cross-regional validation to enhance model reliability and policy relevance.…”
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Mapping the covariate-adjusted spatial effects of childhood anemia in Ethiopia using a semi-parametric additive model by Seyifemickael Amare Yilema, Seyifemickael Amare Yilema, Yegnanew A. Shiferaw, Najmeh Nakhaeirad, Ding-Geng Chen, Ding-Geng Chen

“…Each predictor variable was spatially adjusted using non-parametric smoothing techniques based on geolocation parameters, and corresponding maps for each predictor.ResultsA regularized random forest techniques was employed to identify the most influential predictors of childhood anemia and enhance the model predictive performance. …”
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Spatial Analysis of Dust Storms in Iran based on Climatic and Vegetation Characteristics by Farzaneh Borzou, Hasan Zolfaghari, Jafar Masoompour Samakosh, Jalil Sahraei

“…The present study aims to identify and spatially analyze the sources of Iran dust storms by using the National Centers for Environmental Prediction (NCEP/DOE); European Centre for Medium-Range Weather Forecasts operational (ECMWF) ERA-Interim reanalysis datasets and the records of 52 synoptic stations from 1984 to 2016. …”
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Method for prediction magnetic silencing of uncertain energy-saturated extended technical objects in prolate spheroidal coordinate system by B. I. Kuznetsov, A. S. Kutsenko, T. B. Nikitina, I. V. Bovdui, K. V. Chunikhin, V. V. Kolomiets

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A meta-learning approach to improving transferability for freeway traffic crash risk prediction by Chenlei Liao, Xiqun (Michael) Chen

“…Due to the limited availability of crash data in some freeway sections, model transferability of crash risk prediction has become an essential topic in traffic safety research. …”
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Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model by Chenxiang Wang, Pengfei Zhang, Wenbin Shen, Jiayao Wang

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