Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms

Geoderma ◽  
2019 ◽  
Vol 340 ◽  
pp. 55-69 ◽  
Author(s):  
Mahdis Amiri ◽  
Hamid Reza Pourghasemi ◽  
Gholam Abbas Ghanbarian ◽  
Sayed Fakhreddin Afzali
Keyword(s):  
Machine Learning ◽  
Spatial Modeling ◽  
Learning Algorithms ◽  
Gully Erosion ◽  
Machine Learning Algorithms ◽  
Effective Factors ◽  
Boruta Algorithm

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.

scholarly journals ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS

2021 ◽  
Vol 13 (2) ◽  
pp. 211
Author(s):  
Rahmat Azul Mizan ◽  
Prima Widayani ◽  
Nur Mohammad Farda
Keyword(s):  
Machine Learning ◽  
Dengue Fever ◽  
Spatial Modeling ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Climate Data ◽  
Major Health Problem

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image

scholarly journals Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 469-498 ◽  
Author(s):  
Alireza Arabameri ◽  
Subodh Chandra Pal ◽  
Romulus Costache ◽  
Asish Saha ◽  
Fatemeh Rezaie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Ensemble Machine Learning

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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