جستجوی مقالات مرتبط با کلیدواژه « random forest algorithm » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

The negative effects of soil salinity on natural and human environments have turned such phenomenon into one of the serious threats to the sustainable management of arid and semi-arid areas. The present study aims to evaluate the spatial-temporal variations in land use/land cover and soil salinity in a part of Sistan basin located in the arid regions of southeastern Iran, which has been exposed to the phenomenon of soil salinity during the recent years. To this aim, land use/land cover and soil salinity maps were prepared for 1989 and 2019 using the measurements obtained from ground sampling and instruments such as remote sensing (RS) and geographic information system (GIS). Based on the results, the normalized average soil salinity was 0.322 during 1989, reaching 0.52 during 2019 with a growth of 0.188. In addition, comparing the trend of salinity increase and land use/land cover variations in the region indicates that such factors affect each other significantly. Further, the conversion of land use/land cover from irrigated agricultural uses and water bodies to bare lands, rainfed agriculture, and man-made areas has decreased the vegetation cover and water areas, leading to an increase in soil salinity due to the erosion created by the 120- day wind of Sistan and sedimentation of salt particles in the whole region. Chah-nimehs have not been able to fully compensate for the adverse effects generated by the destruction of habitats and drying up of a part of Hamun Lake and Hirmand River, despite their slight success in reducing the environmental obstacles.

The aim of this study was to model the landslide susceptibility using the Random Forest Machine learning technique and prioritization of effective factors on landslide occurrence in Bar watershed in Khorasan Razavi province. The random forest algorithm is based on a bunch of decision trees and is currently one of the best machine learning algorithms. For this purpose, a landslide inventory map was created with 73 historical landslides, which was randomly divided into two datasets for model training (70%) and model testing (30%). A total of 16 landslide-conditioning factors were considered for the susceptibility landslide mapping. The random forest algorithm was run and a landslide susceptibility map was prepared. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. The results of evaluation indicated that the success and prediction rates of the model were 90% in training and 89% in validation, respectively. These results confirm the ability of random forest method for prediction of landslide susceptibility models. Also, prioritization of the effective factors showed that the slope length and slope had the highest effect on landslide occurrence. Based on the results of the random forest model, 23.7% of the study area is located in a very high and high sensitivity zone.

Flood damage assessment is often necessary for early flood management. To this end, this paper provides a framework of rapid estimation of flood damage and identification the flooded areas in March 2020 using Sentinel-1 satellite data. To this end, in the present study, after applying the necessary pre-processing in SNAP6 software, the backscattering coefficient, or sigma naught for two images related to before and after the flood occurrence was extracted. The backscattering coefficient histogram was used to separate the image into two classes including water and non-water and the threshold of 0.01 was obtained based on it. Then, by applying mathematical operations on both backscattering images, the binary image of water and non-water was prepared and the flooded areas were determined based on the difference between the two images. After detecting the flooded areas, Sentinel images were classified into three classes including waterbody before flood, flooded area and other lands using supervised classification algorithms. The results indicated the high accuracy of the Random Forest algorithm with kappa of 0.92 compared to other algorithms. By overlaying the land use and flooded areas maps, the inundation percentage for each land use was determined. According to the results, bare lands with 27.9 percent, residential land with 16 percent and rangelands with 12 percent had the highest inundation percentage, respectively.