A novel study on machine learning algorithm‐based cardiovascular disease prediction
Cardiovascular disease (CVD) is a life‐threatening disease rising considerably in the world.
Early detection and prediction of CVD as well as other heart diseases might protect many …
Early detection and prediction of CVD as well as other heart diseases might protect many …
[HTML][HTML] Coupling coordination degree between social-economic development and water environment: A case study of Taihu lake basin, China
L Xu, SS Chen - Ecological Indicators, 2023 - Elsevier
Exploring the coupled and coordinated development of social-ecological systems is an
important basis for promoting integrated watershed management. Based on the framework …
important basis for promoting integrated watershed management. Based on the framework …
Deforestation, forest degradation, and land use dynamics in the Northeastern Ecuadorian Amazon
S López - Applied Geography, 2022 - Elsevier
Land cover transformations throughout the Amazon basin have significantly intensified in
recent decades due to increased human activity. Using a land change science approach …
recent decades due to increased human activity. Using a land change science approach …
A forest of forests: a spatially weighted and computationally efficient formulation of geographical random forests
S Georganos, S Kalogirou - ISPRS International Journal of Geo …, 2022 - mdpi.com
The aim of this paper is to present developments of an advanced geospatial analytics
algorithm that improves the prediction power of a random forest regression model while …
algorithm that improves the prediction power of a random forest regression model while …
A geographically weighted random forest approach to predict corn yield in the US corn belt
Crop yield prediction before the harvest is crucial for food security, grain trade, and policy
making. Previously, several machine learning methods have been applied to predict crop …
making. Previously, several machine learning methods have been applied to predict crop …
Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across
spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk …
spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk …
[HTML][HTML] The power of on-farm data for improved agronomy
Advances in technology and analytics to support data-driven agriculture has important
implications for global food security and environmental sustainability. However, relatively …
implications for global food security and environmental sustainability. However, relatively …
[HTML][HTML] Examining the spatially varying relationships between landslide susceptibility and conditioning factors using a geographical random forest approach: A case …
Landslide susceptibility assessment is an important means of helping to reduce and
manage landslide risk. The existing studies, however, fail to examine the spatially varying …
manage landslide risk. The existing studies, however, fail to examine the spatially varying …
[HTML][HTML] Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US
Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial
dependence and spatial heterogeneity in the crash data. This study makes the best of …
dependence and spatial heterogeneity in the crash data. This study makes the best of …
Spatiotemporal variations in meteorological influences on ambient ozone in China: A machine learning approach
Considering the increase in ambient ozone (O 3) levels with harmful health effects, this study
aims to evaluate the spatiotemporal variations in meteorological influences on the daily …
aims to evaluate the spatiotemporal variations in meteorological influences on the daily …