Spatial Modeling of Maritime Risk Using Machine Learning

Risk Analysis ◽  
2021 ◽  
Author(s):  
Andrew Rawson ◽  
Mario Brito ◽  
Zoheir Sabeur
Keyword(s):  
Machine Learning ◽  
Spatial Modeling

Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia

2020 ◽  
Vol 699 ◽  
pp. 134230 ◽  
Author(s):  
Omid Rahmati ◽  
Fatemeh Falah ◽  
Kavina Shaanu Dayal ◽  
Ravinesh C. Deo ◽  
Farnoush Mohammadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Modeling ◽  
Learning Approaches ◽  
The South ◽  
East Region

Spatial Modeling and Geovisualization of Rental Prices for Real Estate portals

2017 ◽  
Vol 8 (2) ◽  
pp. 78-91
Author(s):  
Harald Schernthanner ◽  
Hartmut Asche ◽  
Julia Gonschorek ◽  
Lasse Scheele
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Spatial Statistics ◽  
Spatial Modeling ◽  
Status Quo ◽  
Minor Role ◽  
Spatial Reference ◽  
Spatial Methods ◽  
Address Data ◽  
A Minor

From a geoinformation science perspective real estate portals apply non-spatial methods to analyse and visualise rental price data. Their approach shows considerable shortcomings. Portal operators neglect real estate agents' mantra that exactly three things are important in real estates: location, location and location (Stroisch, 2010). Although real estate portals retacord the spatial reference of their listed apartments, geocoded address data is used insufficiently for analyses and visualisation, and in many cases the data is just used to “pin” map the listings. To date geoinformation science, spatial statistics and geovisualization play a minor role for real estate portals in analysing and visualising their housing data. This contribution discusses the analytical and geovisual status quo of real estate portals and addresses the most serious deficits of the employed non-spatial methods. Alternative analysing approaches from geostatistics, machine learning and geovisualization demonstrate potentials to optimise real estate portals´ analysing and visualisation capacities.

Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques

Geoderma ◽  
2017 ◽  
Vol 305 ◽  
pp. 314-327 ◽  
Author(s):  
Wei Chen ◽  
Hamid Reza Pourghasemi ◽  
Aiding Kornejady ◽  
Ning Zhang
Keyword(s):  
Machine Learning ◽  
Spatial Modeling ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms

2021 ◽  
Vol 13 (16) ◽  
pp. 3222
Author(s):  
Seyed Vahid Razavi-Termeh ◽  
Abolghasem Sadeghi-Niaraki ◽  
Soo-Mi Choi
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Spatial Modeling ◽  
Learning Algorithms ◽  
Traffic Volume ◽  
Machine Learning Algorithms ◽  
Weight Of Evidence ◽  
Adaptive Boosting ◽  
Adaboost Algorithm ◽  
Ensemble Machine Learning

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.

Sign in / Sign up

Export Citation Format

Share Document