| 1. |
- Shahabi, Himan, et al.
(författare)
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Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms
- 2023
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 15:12
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Tidskriftsartikel (refereegranskat)abstract
- Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management.
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| 2. |
- Shahabi, Himan, et al.
(författare)
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Satellite-Synoptic Monitoring of Dominant Dust Entering Western Iran
- 2023
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Ingår i: Journal of Sensors. - : Hindawi Publishing Group. - 1687-725X .- 1687-7268. ; 2023
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Tidskriftsartikel (refereegranskat)abstract
- Dust storm in Iran's western regions has been one of its major environmental problems in recent years, which has not only turned into a yearly phenomenon but is also expanding. This study investigated two events of dominant dust in southwestern Iran using moderate resolution imaging spectroradiometer imagery, Reanalysis Datasets (meteorological fields and atmospheric compositions), in both hot (July 2, 2008) and cold (February 18, 2017) seasons. After radiometric correction and calculation of brightness temperature as well as the reflective and thermal behavior of dust, the research results showed that the detection of dominant dust entering was 0.645 mu m (visible red) and 0.858 mu m (near-infrared) in the reflective ranges and 3.959, 8.55, 11.03, and 12.02 mu m in the thermal ranges. Synoptically, the lower values for mean sea level pressure from the east Mediterranean along Syria and Iraq to the southwest and Central Asia facilitate a convergence condition in the lower troposphere that induces strong northwesterlies, Shamal winds, over the Middle East toward the Persian Gulf, forming a more expansive aerosol hotspot over southwest Iran. However, on a cold day, high dust events in Arabia and south Iran are related to the ongoing high pressure, which is accompanied by a subtropical jet, following anticyclonic circulation toward southwestern Iran.
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| 3. |
- Sharifipour, Behzad, et al.
(författare)
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Rangeland species potential mapping using machine learning algorithms
- 2023
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Ingår i: Ecological Engineering. - : Elsevier. - 0925-8574 .- 1872-6992. ; 189
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Tidskriftsartikel (refereegranskat)abstract
- Documenting habitats of rangeland plant species is required to properly manage rangelands and to understand ecosystem processes. A reliable rangeland species potential map can help managers and policy makers design a sustainable grazing system on rangelands. The aim of this study is to map the plant species in the Qurveh City rangelands, Kurdistan Province, Iran, using state-of-the-art machine learning algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes (NB), Bayes Net (BN) and Classification and Regression Tree (CART). A total of 185 rangeland species were used in the study, together with 20 conditioning factors, to build and validate models. The One-R feature section technique and multicollinearity test were used, respectively, to determine the most important factors and correlations between them. Model validation was performed using sensitivity, specificity, accuracy, F1-measure, Matthews correlation coefficient (MCC), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). Results showed that topographic wetness index (TWI), slope angle, elevation, soil phosphorus and soil potassium were the five most important factors to increase the rangeland plants habitat suitability. The Naïve Bayes algorithm (AUC = 0.782) had the highest performance and prediction accuracy and best consistency across the species in the investigated rangeland, followed by the SVM (AUC = 0.763), ANN (AUC = 0.762), CART (AUC = 0.627), and BN (AUC = 0.617) models.
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