Comparison of machine learning models to prioritize susceptible areas to dust production

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Darvand Serveh; KHOSRAVI HASSAN; Keshtkar Hamidreza; ZEHTABIAN GHOLAMREZA; RAHMATI OMID

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Journal: Journal of Range and Watershed Managment Year:2021 | Volume:74 | Issue:1 Page(s): 53-68

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Title

Comparison of machine learning models to prioritize susceptible areas to dust production

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Keywords

AlborzQ1

qazvinQ1

Satellite imagesQ1

DustQ1

Machine learningQ1

ROC CurveQ1

Abstract

The purpose of this study was to compare Machine learning models including Support Vector Machine, Classification and Regression Tree, Random Forest, and Multivariate Discriminate Analysis to prioritize susceptible areas to Dust production. To determine the Dust days, hourly meteorological data of Alborz and qazvin provinces and Satellite images of the same days for the period 2000 to 2019 were used. 420 Dust collection points were identified and the map of their distribution was prepared. The maps of factors affecting the occurrence of Dust, including landuse map, soil orders map, slope map, slope aspect map, elevation map, vegetation map, topographic surface moisture, topographic surface ratio, and geology mam were prepared. Using the mentioned models, the impact of each of the effective factors of Dust was determined and prioritization maps of Dust harvesting areas were prepared. Models were evaluated using the ROC Curve. According to the results, the elevation factor is more important in all models than the other parameters used in the model. The modeling results also showed that the Random Forest )RF( and Multivariate Discriminate Analysis (MDA) models had the highest values of accuracy (0. 96), precision (0. 94), Probability of Detection (POD) (0. 98), and False Alarm Ratio (FAR) (0. 051) compared to the others. The performance of the RF and MDA models is better than the other models, followed by the Support Vector Machine (SVM) and Classification and Regression Tree (CART) models, respectively. Also, in evaluating the models using Receiver Operating Characteristic (ROC), the RF model was selected as the best model.

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APA: Copy

Darvand, Serveh, KHOSRAVI, HASSAN, Keshtkar, Hamidreza, ZEHTABIAN, GHOLAMREZA, & RAHMATI, OMID. (2021). Comparison of machine learning models to prioritize susceptible areas to dust production. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), 74(1 ), 53-68. SID. https://sid.ir/paper/388967/en

Vancouver: Copy

Darvand Serveh, KHOSRAVI HASSAN, Keshtkar Hamidreza, ZEHTABIAN GHOLAMREZA, RAHMATI OMID. Comparison of machine learning models to prioritize susceptible areas to dust production. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES)[Internet]. 2021;74(1 ):53-68. Available from: https://sid.ir/paper/388967/en

IEEE: Copy

Serveh Darvand, HASSAN KHOSRAVI, Hamidreza Keshtkar, GHOLAMREZA ZEHTABIAN, and OMID RAHMATI, “Comparison of machine learning models to prioritize susceptible areas to dust production,” JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), vol. 74, no. 1 , pp. 53–68, 2021, [Online]. Available: https://sid.ir/paper/388967/en

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