Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
Understanding the associations between the built environment and road traffic CO 2
emissions is crucial for develo** strategies to mitigate carbon emissions. However …
emissions is crucial for develo** strategies to mitigate carbon emissions. However …
[HTML][HTML] An ensemble framework for explainable geospatial machine learning models
L Liu - International Journal of Applied Earth Observation and …, 2024 - Elsevier
Analyzing spatially varying effects is pivotal in geographic analysis. However, accurately
capturing and interpreting this variability is challenging due to the increasing complexity and …
capturing and interpreting this variability is challenging due to the increasing complexity and …
An ensemble spatial prediction method considering geospatial heterogeneity
Ensemble learning synthesizes the advantages of different models and has been widely
applied in the field of spatial prediction. However, the nonlinear constraints of spatial …
applied in the field of spatial prediction. However, the nonlinear constraints of spatial …
DKNN: deep kriging neural network for interpretable geospatial interpolation
Geospatial interpolation plays a pivotal role in spatial analysis because it provides high-
quality data support for various spatiotemporal data mining (STDM) tasks. However …
quality data support for various spatiotemporal data mining (STDM) tasks. However …
A high-resolution land surface temperature downscaling method based on geographically weighted neural network regression
M Liang, L Zhang, S Wu, Y Zhu, Z Dai, Y Wang, J Qi… - Remote Sensing, 2023 - mdpi.com
Spatial downscaling is an important approach to obtain high-resolution land surface
temperature (LST) for thermal environment research. However, existing downscaling …
temperature (LST) for thermal environment research. However, existing downscaling …
An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of ** the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19
According to the World Health Organization (WHO), COVID-19 has caused more than 6.5
million deaths, while over 600 million people are infected. With regard to the tools and …
million deaths, while over 600 million people are infected. With regard to the tools and …
[HTML][HTML] Locally varying geostatistical machine learning for spatial prediction
Abstract Machine learning methods dealing with the spatial auto-correlation of the response
variable have garnered significant attention in the context of spatial prediction. Nonetheless …
variable have garnered significant attention in the context of spatial prediction. Nonetheless …
GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal non-stationarity
Z Yin, J Ding, Y Liu, R Wang, Y Wang… - Geoscientific Model …, 2024 - gmd.copernicus.org
Spatiotemporal regression is a crucial method in geography for discerning spatiotemporal
non-stationarity in geographical relationships, which has found widespread application …
non-stationarity in geographical relationships, which has found widespread application …
A fuzzy rough sets-based data-driven approach for quantifying local and overall fuzzy relations between variables for spatial data
H Bai, J **g, D Li, Y Ge - Applied Soft Computing, 2024 - Elsevier
Exploring the relationships between variables is a crucial component in comprehending
geographical phenomena. Most existing methods ignore the vagueness hidden in spatial …
geographical phenomena. Most existing methods ignore the vagueness hidden in spatial …