РИСК ЭРОЗИИ ПОЧВ В ТАШКЕНТСКОЙ ОБЛАСТИ: DEM И GIS-АНАЛИЗ
DOI:
https://doi.org/10.56292/SJFSU/vol31_iss6/a265Ключевые слова:
овражная эрозия, ГИС, ЦМР (DEM), Weighted Overlay, уклон, накопление стока, экспозиция склонов, Ташкент-ская область.Аннотация
В статье оценивается опасность овражной эрозии в Ташкентской области с применением ГИС и проводится сравнение модели Weighted Overlay с инвентарной картой оврагов по данным Google Earth Pro. Уклон, накопление стока и экспозиция склонов, полученные из ЦМР, объединены с весами 40–40–20 %, что позволяет выделить зоны низкой, средней и высокой опасности. Класс «высокая опасность» охватывает около 78–82 % картированных оврагов и служит надежной основой для планирования противоэрозионных мероприятий.
Библиографические ссылки
1. Aboutaib F., Arabameri A., Hssaisoune M., Rachid R., Boudad L., Pradhan B., Avand M., Tiefenbacher J.P. Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion // Frontiers in Environmental Science. – 2023. – Vol. 11. – Art. 1207027. – DOI: 10.3389/fenvs.2023.1207027.
2. Amiri M., Pourghasemi H.R., Ghanbarian G.A., Afzali S.F. Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modelling and mapping using three machine learning algo-rithms // Geoderma. – 2019. – Vol. 340. – P. 55–69. – DOI: 10.1016/j.geoderma.2018.12.042.
3. Aouragh M.H., El Jazouli A., Barakat A., El Baghdadi M., Khellouk R., Ettaqy A., Teodoro A.C. Remote sensing and GIS-based machine learning models for spatial gully erosion prediction: A case study of Rdat watershed in Sebou basin, Morocco // Remote Sensing Applications: Society and Environment. – 2023. – Vol. 30. – Art. 100939. – DOI: 10.1016/j.rsase.2023.100939.
4. Arabameri A., Pradhan B., Lombardo L. Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling // Catena. – 2019. – Vol. 183. – Art. 104223. – DOI: 10.1016/j.catena.2019.104223.
5. Arabameri A., Cerda A., Pradhan B., Tiefenbacher J.P., Lombardo L., Bui D.T. A methodological compari-son of head-cut based gully erosion susceptibility models: Combined use of statistical and artificial intelligence // Ge-omorphology. – 2020. – Vol. 359. – Art. 107136. – DOI: 10.1016/j.geomorph.2020.107136.
6. Avand M., Janizadeh S., Naghibi S.A., Pourghasemi H.R., Khosrobeigi Bozchaloei S., Blaschke T. A com-parative assessment of Random Forest and k-Nearest Neighbor classifiers for gully erosion susceptibility mapping // Water. – 2019. – Vol. 11, No. 10. – Art. 2076. – DOI: 10.3390/w11102076.
7. Conoscenti C., Di Maggio C., Rotigliano E. Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy) // Geomorphology. – 2014. – Vol. 204. – P. 399–411. – DOI: 10.1016/j.geomorph.2013.08.021.
8. Conoscenti C., Agnesi V., Cama M., Caraballo-Arias N.A., Rotigliano E. Assessment of gully erosion sus-ceptibility using multivariate adaptive regression splines and accounting for terrain connectivity // Land Degradation & Development. – 2018. – Vol. 29, No. 3. – P. 724–736. – DOI: 10.1002/ldr.2772.
9. Javidan N., Kavian A., Pourghasemi H.R., Conoscenti C., Jafarian Z. Gully erosion susceptibility mapping using multivariate adaptive regression splines-replications and sample size scenarios // Water. – 2019. – Vol. 11, No. 11. – Art. 2319. – DOI: 10.3390/w11112319.
10. Lei X., Chen W., Avand M., Janizadeh S., Kariminejad N., Shahabi H., Costache R., Shirzadi A., Mosavi A. GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran // Remote Sensing. – 2020. – Vol. 12, No. 15. – Art. 2478. – DOI: 10.3390/rs12152478.
11. Liu C., Wang J., Li Z., Zhao G., Gao J., Zhang Q., Li H., Cheng M., Zhang S. Gully erosion susceptibility as-sessment based on machine learning: A case study of watersheds in Tuquan County in the black soil region of North-east China // Catena. – 2023. – Vol. 222. – Art. 106798. – DOI: 10.1016/j.catena.2022.106798.
12. Rahmati O., Haghizadeh A., Pourghasemi H.R., Noormohamadi F. Gully erosion susceptibility mapping: The role of GIS-based bivariate statistical models and their comparison // Natural Hazards. – 2016. – Vol. 82, No. 2. – P. 1231–1258. – DOI: 10.1007/s11069-016-2239-7.
13. Saha S., Roy J., Arabameri A., Blaschke T., Bui D.T. Machine learning-based gully erosion susceptibility mapping: A case study of Eastern India // Sensors. – 2020. – Vol. 20, No. 5. – Art. 1313. – DOI: 10.3390/s20051313.
14. Tien Bui D., Shirzadi A., Shahabi H., Chapi K., Khaledi Darvishan A., Pham B.T., Chen W., Khosravi K., Pradhan B., Panahi M., Lee S. A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran) // Sensors. – 2019. – Vol. 19, No. 11. – Art. 2444. – DOI: 10.3390/s19112444.
15. Titti G., Camici S., Brocca L. Cloud-based interactive susceptibility modeling of gully erosion in Google Earth Engine // International Journal of Applied Earth Observation and Geoinformation. – 2022. – Vol. 115. – Art. 103089. – DOI: 10.1016/j.jag.2022.103089.
16. Yang A., Wang C., Pang G., Long Y., Wang L., Cruse R.M., Yang Q. Gully erosion susceptibility mapping in highly complex terrain using machine learning models // ISPRS International Journal of Geo-Information. – 2021. – Vol. 10, No. 10. – Art. 680. – DOI: 10.3390/ijgi10100680.
Загрузки
Опубликован
Выпуск
Раздел
Лицензия
Copyright (c) 2025 Научный вестник Ферганский государственный университета

Это произведение доступно по лицензии Creative Commons «Attribution-NonCommercial-NoDerivatives» («Атрибуция — Некоммерческое использование — Без производных произведений») 4.0 Всемирная.