scholarly journals Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

2021 ◽  
Vol 13 (18) ◽  
pp. 10239
Author(s):  
Farbod Farhangi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Seyed Vahid Razavi-Termeh ◽  
Soo-Mi Choi
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Learning Algorithms ◽  
Time Of Day ◽  
Machine Learning Algorithms ◽  
Risk Mapping ◽  
Accident Risk ◽  
National Scale ◽  
Road Type ◽  
The City

Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.

Machine-learning algorithms for land use dynamics in Lake Haramaya Watershed, Ethiopia

2021 ◽  
Author(s):  
Gezahegn Weldu Woldemariam ◽  
Degefie Tibebe ◽  
Tesfamariam Engida Mengesha ◽  
Tadele Bedo Gelete
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Land Use Dynamics

scholarly journals Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

2020 ◽  
Vol 12 (7) ◽  
pp. 1135 ◽  
Author(s):  
Swapan Talukdar ◽  
Pankaj Singha ◽  
Susanta Mahato ◽  
Shahfahad ◽  
Swades Pal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Learning Algorithms ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Land Use Land Cover ◽  
Accuracy Level ◽  
Parametric Classifier

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Assessment of machine learning algorithms in land use classification

2022 ◽  
pp. 97-104
Author(s):  
Hassan Khavarian Nehzak ◽  
Maryam Aghaei ◽  
Raoof Mostafazadeh ◽  
Hamidreza Rabiei-Dastjerdi
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Land Use Classification
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