Drone-assisted climate-smart agriculture (DACSA): A spatially-based outcome prediction model as an initial approach to track yield changes in shallot planting areas

“…Machine learning algorithms were employed to make predictions, and the yield projections were integrated into spatial maps. …”
Get full text

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. …”
Get full text

Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration by Junjie Jiang, Lingxia Feng, Junguo Hu, Chao Zhu

“…Additionally, we found significant biases in the annual Rs calculated by area weighting based on climate and ecosystem classifications because these factors characterise spatial heterogeneity differently. Such dynamics should be considered when modelling global Rs and analysing the results because they can help improve the estimation accuracy of global Rs prediction models.…”
Get full text

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. …”
Get full text

Cognitive and Spatial Forecasting Model for Maritime Migratory Incidents: SIFM by Donatien Agbissoh Otote, Antonio Vázquez Hoehne

Get full text

Full-scale Educational Model in Arafa prayer and Educational Principles related to it by mahdiyeh keshani, abazar kafimosavi

“…The aim of the present study was to explain the full-scale educational model in the Arafa prayer and to infer educational principles based on it. …”
Get full text

Predictive Modeling of Surface Subsidence Considering Different Environmental Risk Zones by Yunsong Li, Yongjun Qin, Liangfu Xie, Yangchun Yuan, Jie Ran

“…Adopt four different noise reduction algorithms for data noise reduction on the raw data of the monitoring points at the intervals of different risk zones, and combine the time series prediction as well as the deep learning prediction method to get the prediction model for environmental risk zoning based on the environmental risk zoning. …”
Get full text

Study on Key Influencing Factors of Carbon Emissions from Farmland Resource Utilization in Northeast China Under the Background of Energy Conservation and Emission Reduction by Mulin Sun, Yuhao Fu, Mingyao Sun, Run Huang, Yun Teng

“…A gray prediction model is constructed to predict the carbon emissions from the utilization of farmland resources in the next 10 years, and the logarithmic mean Divisia index model is used to analyze the effects of the various influencing factors on the carbon emissions from farmland utilization. …”
Get full text

ASSESSMENT OF ROMANIAN ALPINE HABITATS SPATIAL SHIFTS BASED ON CLIMATE CHANGE PREDICTION SCENARIOS by ADRIAN CONSTANTINESCU, JENICĂ HANGANU, ANTHONY LEHMANN, NICOLAS RAY

“…Maxent and BIOCLIM were used to create spatial distribution models for Mesophilous oligotrophic mountain pasture and Subalpine oligotrophic pastures. …”
Get full text

Prediction of spatial yield strength distribution in Al–Mg–Sc alloy fabricated by coaxial laser wire directed energy deposition by Xianyue Liu, Yuelan Di, Gang Wang, Longcen Ji, Yining Yuan, Yiming Rong

“…The spatially heterogeneous thermal history during additive manufacturing (AM) leads to variations in the mechanical properties of the fabricated parts. …”
Get full text

Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction by Zhifei Yang, Zeyang Li, Jia Zhang

“…This research advances traffic prediction methodologies through its integrated approach to dynamic spatial correlation modeling and bidirectional temporal learning, providing valuable insights for intelligent transportation system development.…”
Get full text

Integrating machine learning and spatial clustering for malaria case prediction in Brazil’s Legal Amazon by Kayo Henrique de Carvalho Monteiro, Élisson da Silva Rocha, Luis Augusto Morais, Elton Gino Santos, Sebastião Rogerio da S. Neto, Vanderson Sampaio, Patricia Takako Endo

“…The integration of K-means clustering further improved the model predictive accuracy by accounting for spatial heterogeneity and capturing localized transmission dynamics. …”
Get full text

Single-nucleotide polymorphism analysis accurately predicts multiple impairments in hippocampal activity and memory performance in a murine model of idiopathic autism by Isabel Barón-Mendoza, Luis A. Márquez, Aliesha González-Arenas, Jessica Guzmán-Condado, Vladimir A. Martínez-Rojas, Johaly Anguiano-Buenfil, Montserrat Mejía-Hernández, Jorge Luis Almazán, Leonor Pérez-Martínez, Gustavo Pedraza-Alva, Emilio J. Galván, Angélica Zepeda

Get full text

Modeling to predict cases of hantavirus pulmonary syndrome in Chile. by Elaine O Nsoesie, Sumiko R Mekaru, Naren Ramakrishnan, Madhav V Marathe, John S Brownstein

“…Several studies have estimated risk based on spatial and temporal distribution of cases in relation to climate and environmental variables, but few have considered climatological modeling of HPS incidence for monitoring and forecasting purposes.…”
Get full text

Spatial management of poverty-biodiversity interactions in semi-arid ecosystems by Azam Khosravi Mashizi, Mohsen Sharafatmandrad

“…The results revealed a spatially varied relationship between biodiversity conservation and poverty, with both positive and negative outcomes. …”
Get full text

Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

“…The outcomes demonstrate the TBS model’s ability to accurately predict AQI values. …”
Get full text

Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction by Yutao Luo, Aining Sun, Jiawei Hong

“…In this paper, we propose a driving strategy learning method based on spatial-temporal feature prediction. Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. …”
Get full text

Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction by Feng Chang, Liang Ge, Siyu Li, Kunyan Wu, Yaqian Wang

“…However, accurately predicting future air quality remains a challenging task because of the complex spatial-temporal dependencies of air quality. …”
Get full text

Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…Traditional machine learning methods, while effective in static scenarios, struggle to capture these dependencies, and existing deep learning models often lack the ability to jointly model spatial and temporal relationships. …”
Get full text

Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. …”
Get full text