Determination of Spatial-Temporal Correlation Structure of Troposphere Ozone Data in Tehran City by S.S. Mousavi, M. Mohammadzadeh

“…Spatial-temporal modeling of air pollutants, ground-level ozone concentrations in particular, has attracted recent attention because by using spatial-temporal modeling, can analyze, interpolate or predict ozone levels at any location. …”
Get full text

Data-Supported Prediction of Surface Settlement Behavior on Opencast Mine Dumps Using Satellite-Based Radar Interferometry Observations by Jörg Benndorf, Natalie Merkel, Andre John

“…Satellite-based radar interferometry (InSAR) technology offers highly detailed data on vertical ground movements with a high spatial and temporal resolution. By combining a data-driven approach, using InSAR-generated high-resolution datasets, with model-driven methods such as inverse modeling and classic time–settlement models, the efficient monitoring and prediction of opencast mine dump settlements can be achieved. …”
Get full text

Comparision of Kernel Functions in Geographically Weighted Regression Model: Suicide Data as an Application by Tuba Koç, Pelin Akın

“…In this study, the number of suicides data was used for Turkey’s 81 provinces in 2019.The effects of factors affecting suicide and spatial differences on suicide were analyzed and predicted with geographically weighted regression models (GWR). …”
Get full text

Multi-Source Coordinated Control and Parameter Optimization Methods for Frequency Stability in High-Proportion New Energy Sending-End Systems by JIANG Minghua, LI Ding, KONG Dezhen, WANG Zhuxiu, BAI Ningning, LI Zhongwen

“…Second， a multi-objective model-predictive automatic generation control method was designed， considering system frequency deviations， generation costs， and emission costs. …”
Get full text

Temporal dynamics of SARS-CoV-2 detection in wastewater and population infection trends in Mexico City by Miguel Atl Silva-Magaña, Miguel Atl Silva-Magaña, Marisa Mazari-Hiriart, Adalberto Noyola, Ana C. Espinosa-García, Guillermo de Anda-Jáuregui, Guillermo de Anda-Jáuregui, Enrique Hernández-Lemus

Get full text

Effect of bin width on variogram model accuracy: a case study of teak tree volume specific to Solomon clone in Tawau, Sabah, Malaysia by Johannah Jamalul Kiram, Rossita Mohamad Yunus, Yani Japarudin, Mahadir Lapammu, Olivier Monteuuis, Doreen Kim Soh Goh

Get full text

Enhancing proximal and remote sensing of soil organic carbon: A local modelling approach guided by spectral and spatial similarities by Qi Sun, Pu Shi

“…At large scale, increasing soil heterogeneity complicates the response relationship between soil spectra and SOC, making global models ineffective for local SOC predictions. Here, we propose a local learning approach that searches spectrally and spatially similar samples for site-specific SOC predictive modelling. …”
Get full text

USING THE SLEUTH MODEL TO SIMULATE FUTURE URBAN GROWTH IN THE GREATER EASTERN ATTICA AREA, GREECE by Stylianos Mathioulakis, Yorgos N. Photis

Get full text

USING THE SLEUTH MODEL TO SIMULATE FUTURE URBAN GROWTH IN THE GREATER EASTERN ATTICA AREA, GREECE by Stylianos Mathioulakis, Yorgos N. Photis

Get full text

Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach by Marc Semper, Manuel Curado, Jose Luis Oliver, Jose F. Vicent

“…The results demonstrate that explicitly integrating the spatial component through graphs, alongside temporal sequence modeling, leads to improved prediction accuracy over alternative methods.…”
Get full text

Applications of machine learning in potentially toxic elemental contamination in soils: A review by Yan Li, Bao Xiang, Tianyang Wang, Yinhai He, Xiaoyang Liu, Yancheng Li, Shichang Ren, Erdan Wang, Guanlin Guo

“…In addition, ML algorithms that integrate environmental covariates offer superior performance in spatial predictions compared with traditional geostatistical methods. …”
Get full text

The global incidence rate of type 2 diabetes related chronic kidney disease and predictions by Bayesian age-period-cohort analysis: findings from the Global Burden of Disease Study... by Junpu Yu, Fanhui Luo, Yiwen Zhang, Jingli Yang, Shuxia Yu, Shuxia Yu, Nan Li, Aimin Yang, Li Ma, Jinsheng Li

“…AimsTo evaluate the spatial-temporal changes in the incidence of type 2 diabetes related chronic kidney disease (CKD-T2DM) from 1990 to 2019, categorized by age and sex in 21 regions with different socio-demographic indexes (SDI), and to predict the incidence rate between 2020 and 2030.MethodsData on the burden of CKD-T2DM were obtained from the Global Burden of Disease Study 2019. …”
Get full text

An Octant-Based Multi-Objective Optimization Approach for Lightning Warning in High-Risk Industrial Areas by Marcos Antonio Alves, Bruno Alberto Soares Oliveira, Douglas Batista da Silva Ferreira, Ana Paula Paes dos Santos, Osmar Pinto, Fernando Pimentel Silvestrow, Daniel Calvo, Eugenio Lopes Daher

Get full text

GOAT: a novel global-local optimized graph transformer framework for predicting student performance in collaborative learning by Tianhao Peng, Qiang Yue, Yu Liang, Jian Ren, Jie Luo, Haitao Yuan, Wenjun Wu

“…Abstract Collaborative learning is a prevalent learning method, and modeling and predicting student performance in such paradigms is an important task. …”
Get full text

A VMD-TCN-Based Method for Predicting the Vibrational State of Scaffolding in Super High-Rise Building Construction by Ping Zhu, Gen Liu, Jian Wang, Pengfei Wang

Get full text

Prediction of land cover changes in an Urban City of Bangladesh using artificial neural network-based cellular automata by Tania Yeasmin, Sourav Karmaker, Md Shafiqul Islam, Irteja Hasan, Saifur Rahman, Mahmudul Hasan

“…This study was conducted to evaluate temporal and spatial changes in Land Use and Land Cover (LULC) for the years 1980, 2000, and 2020 and predict future LULC changes. …”
Get full text

Analyzing Taiwanese Traffic Patterns on Consecutive Holidays Through Forecast Reconciliation and Prediction-Based Anomaly Detection Techniques by Mahsa Ashouri, Frederick Kin Hing Phoa, Marzia Angela Cremona

“…Two fundamental features of traffic flow time series – seasonality and spatial autocorrelation – are captured by adding Fourier terms in OLS models, spatial aggregation (as a hierarchical structure mimicking the geographical division into regions, cities, and stations), and a reconciliation step. …”
Get full text

A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network by Ming Jiang, Zhiwei Liu, Yan Xu

“…Additionally, GCRNODE employs a data-independent spatiotemporal memory graph convolutional network to capture the dynamic spatial dependencies in missing data scenarios. The experimental results on three real-world traffic datasets demonstrate that GCRNODE outperforms baseline models in prediction performance under various missing data rates and scenarios. …”
Get full text

Spatial analysis of annual precipitation of Khuzestan province; An approach of spatial regressions analysis by Saeed balyani

“…In this research, for determine of precipitation model and predicting of it with geographical factors e.g. altitude, slope and view shade and latitude- longitude by using spatial regressions analysis such as ordinary least squares (OLS) and geographical weighted regressions(GWR), 13 synoptic stations of Khuzestan province from establishment to 2010 were used. …”
Get full text

Spatial and temporal evolution of carbon stocks in Yulin City under changing environments by Guikai Sun, Yadong Li, Rui Huang, Chongxun Mo

“…It then applies the PLUS model to predict the land use of Yulin City under different scenarios in 2030 and forecasts the future carbon stock, providing a theoretical basis for the city’s future development planning. …”
Get full text