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مدل‌سازی رقومی تغییرات سه‌بعدی شوری خاک با استفاده از الگوریتم‌های یادگیری ماشین در اراضی خشک و نیمه‌خشک دشت قزوین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 52، شماره 7، مهر 1400، صفحه 1915-1929 اصل مقاله (1.44 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2021.323030.668957 | ||
| نویسندگان | ||
| سیدروح اله موسوی1؛ فریدون سرمدیان* 2؛ محمود امید3؛ پاتریک بوگارت4 | ||
| 1دانشجوی دکتری گروه مهندسی علوم خاک، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2استاد گروه مهندسی علوم خاک، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 3استاد گروه مهندسی ماشینهای کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 4استاد دانشکده محیط زیست و علوم زمین، دانشگاه کاتولیک لوون، لوون، بلژیک | ||
| چکیده | ||
| شوری خاک به عنوان یکی از مهم‌ترین شاخصهای کیفیت خاک، نقش مهمی در برنامهریزیهای کاربری و مدیریت اراضی در مناطق خشک و نیمه‌خشک دارد. این پژوهش با هدف مدل‌سازی رقومی تغییرات سطحی و عمقی شوری خاک در پنج عمق استاندارد پروژه جهانی نقشه برداری رقومی خاک (5-0، 15-5، 30-15، 60-30 و 100-60 سانتیمتر) در 60 هزار هکتار از اراضی دشت قزوین با وضوح مکانی 15 متر صورت پذیرفت. مطالعات میدانی شامل نمونهبرداری از 278 خاکرخ بود و هدایت الکتریکی خاکها در آزمایشگاه اندازهگیری شد. انتخاب متغیرهای محیطی، شامل پارامترهای مستخرج از دادههای تصاویر لندست 8، توپوگرافی و لایههای اقلیمی، طبق روش حذف ویژگی برگشتی (RFE) صورت پذیرفت. چهار الگوریتم یادگیری ماشین جنگل تصادفی (RF)، کوبیست (CB)، رگرسیون درخت تصمیم (DTr) و k- نزدیک‌ترین همسایگی (k-NN) برای تهیه نقشه پیشبینی شوری خاک استفاده شد. بر اساس نتایج RFE درنهایت 10 متغیر کمکی در هر عمق انتخاب شدند. نتایج نشان داد که مدل CB در اعماق استاندارد 5-0 و 30-15 سانتیمتر با R2 برابر 92/0 و 85/0 و RMSE برابر 77/4 و 90/7 دسی زیمنس بر متر و مدل RF در اعماق 15-5، 60-30 و 100-60 سانتیمتر مدل با R2 بهتریتب برابر 93/0، 94/0، 96/0 و RMSE 65/6، 10/5 و 20/3 دسی زیمنس بر متر بالاترین مقادیر صحت را نسبت به دو مدل DTr و k-NN داشتند. همچنین در اعماق سطحی متغیرهای کمکی مستخرج از دادههای سنجش دور و در اعماق زیرسطحی پارامترهای اقلیمی و توپوگرافی بیشترین ارتباط را با تغییرات شوری داشتند. بطور کلی مدلهای RF و CB به همراه متغیرهای محیطی مناسب بخوبی توانستند تغییرات شوری را در اعماق استاندارد موردمطالعه ارائه نمایند. | ||
| کلیدواژه‌ها | ||
| شوری خاک؛ متغیرهای محیطی؛ نقشه‌برداری رقومی؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain | ||
| نویسندگان [English] | ||
| Sayed Roholla Mousavi1؛ Fereydoon Sarmadian2؛ Mahmoud Omid3؛ Patrick Bogaert4 | ||
| 1Ph.D. Student of Soil Resources Management,, ,Science and soil Engineering Department,, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University, of Tehran. Karaj, Iran. | ||
| 2Professor of Soil and Science Engineering Department,, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| 3Professor of Agricultural Machinery Engineering Department, Faculty of Agricultural Engineering and Technology, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran | ||
| 4Professor of Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium | ||
| چکیده [English] | ||
| Soil salinity, as one of the most important indicators of soil quality, has crucial roles in land use planning and land management in arid and semi-arid regions. The aim of this study was to model soil salinity at five standard depth (0-5, 5-15, 15-30, 30-60, and 60-100 cm) of global digital soil mapping project in 60,000 hectares of Qazvin plain with spatial resolution of 15m. Field studies included a sampling of 278 soil profiles and then the EC was measured in the laboratory. The recursive feature elimination (RFE) method was employed to select environmental covariates including parameters extracted from Landsat 8 image (OLI/TIRS) data, topography, and climatic parameters. Four machine learning algorithms as random forest (RF), cubist (CB), decision tree regression (DTr), and k-nearest neighbors (k-NN) were applied for predicting and mapping soil salinity. According to RFE, 10 covariates were chosen for each standardized depth. The results of modeling showed that the CB model at the depth of 0-5 and 15-30 cm with R2 values of 0.92 and 0.85 and RMSE 4.77 and 7.90 dS/m and the RF model at depths of 5-15, 30-60, and 60-100 cm with R2 values of 0.93, 0.94, 0.96 and RMSE 6.65, 5.10 and 3.20 dS/m, respectively, had the highest accuracy compared to two other models i.e., DTr and k-NN. Furthermore, the covariates extracted from RS data had more impact on topsoil salinity prediction while the climate and topographic attributes influence subsurface soil salinity. Generally, The RF and CB models along with appropriate environmental covariates were able to present salinity variation of study standard depths. | ||
| کلیدواژه‌ها [English] | ||
| Soil salinity, Environmental covariates, Digital soil mapping, Machine learning | ||
| مراجع | ||
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Abedi, F., Amirian‐Chakan, A., Faraji, M., Taghizadeh‐Mehrjardi, R., Kerry, R., Razmjoue, D., & Scholten, T. (2021). Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models. Land Degradation & Development, 32(3), 1540-1554. Allbed, A., & Kumar, L. (2013). Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in remote sensing, 2013. Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B., Hong, S. Y., ... & Zhang, G. L. (2014). GlobalSoilMap: Toward a fine-resolution global grid of soil properties. Advances in agronomy, 125, 93-134. Azabdaftari, A., & Sunarb, F. (2016). Soil salinity mapping using multitemporal Landsat data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 7, 3-9. Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, 24-31. Chen, Y., Qiu, Y., Zhang, Z., Zhang, J., Chen, C., Han, J., & Liu, D. (2020). Estimating salt content of vegetated soil at different depths with Sentinel-2 data. PeerJ, 8, e10585. Da Silva Chagas, C., de Carvalho Junior, W., Bhering, S. B., & Calderano Filho, B. (2016). Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. Catena, 139, 232-240. Daliakopoulos, I. N., Tsanis, I. K., Koutroulis, A., Kourgialas, N. N., Varouchakis, A. E., Karatzas, G. P., & Ritsema, C. J. (2016). The threat of soil salinity: A European scale review. Science of the Total Environment, 573, 727-739. El Hafyani, M., Essahlaoui, A., El Baghdadi, M., Teodoro, A. C., Mohajane, M., El Hmaidi, A., & El Ouali, A. (2019). Modeling and mapping of soil salinity in Tafilalet plain (Morocco). Arabian journal of geosciences, 12(2). Eswaran, H., Lal, R., & Reich, P. F. (2019). Land degradation: an overview. Response to land degradation, 20-35. Forkuor, G., Hounkpatin, O. K., Welp, G., & Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PloS one, 12(1), e0170478. Guo, B., Zang, W., Luo, W., Wen, Y., Yang, F., Han, B., ... & Yang, X. (2020). Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image. Geomatics, Natural Hazards and Risk, 11(1), 288-300. Hengl, T., Heuvelink, G. B., Kempen, B., Leenaars, J. G., Walsh, M. G., Shepherd, K. D., ... & Tondoh, J. E. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PloS one, 10(6), e0125814. Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B. and Guevara, M.A., 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), p. e0169748. Iran Meteorological Organization. (2018). Climate Information, Qazvin synoptic station: Qazvin, Iran. Available at: http://www.irimo.ir/eng/index.php. Jalali, V. R., & Homaee, M. (2011). A nonparametric model by using k-nearest neighbor technique for predicting soil saturated hydraulic conductivity. Journal of water and soil (agricultural sciences and technology). 2011 [cited 2021april23];25(2):347-355. Karamooz, M., and Araghinejad, Sh. 2005. Advanced Hydrology. Amirkabir University Press, 464p. (In Persian). Kingsley, J., Lawani, S. O., Esther, A. O., Ndiye, K. M., Sunday, O. J., & Penížek, V. (2019). Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models. Modern Applied Science, 13(10). Kuhn, M., Weston, S., Keefer, C., & Coulter, N. (2016). C code for Cubist. Cubist: Rule- and instance-based regression modeling. In: R Package Version 0.0.19,https://CRAN.Rproject.org/package=Cubist. Kuhn, M., Weston, S., Keefer, C., & Coulter, N. (2012). Cubist models for regression. R package Vignette R package version 0.0, 18. Litalien, A., & Zeeb, B. (2020). Curing the earth: A review of anthropogenic soil salinization and plant-based strategies for sustainable mitigation. Science of the Total Environment, 698, 134235. McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117, 3–52. Mohammadifar, A., Gholami, H., Golzari, S., & Collins, A. L. (2021). Spatial modelling of soil salinity: deep or shallow learning models? Environmental Science and Pollution Research, 1-19. Mousavi, S. R., Sarmadian, F., & Rahmani, A. (2020). Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain. Iranian Journal of Soil and Water Research, 50(10), 2525-2538. (In Farsi). Mousavi, S., Sarmadian, F., Alijani, Z., & Taati, A. (2017). Land suitability evaluation for irrigating wheat by geopedological approach and geographic information system: A case study of Qazvin plain, Iran. Eurasian Journal of Soil Science, 6(3), 275-284 Naumann, J. C., Young, D. R., & Anderson, J. E. (2009). Spatial variations in salinity stress across a coastal landscape using vegetation indices derived from hyperspectral imagery. Plant Ecology, 202(2), 285-297. Nazari, S., Rostaminia, M., Ayoubi, S., Rahmani, A., & Mousavi, S. R. (2020). Efficiency of Different Feature Selection Methods in Digital Mapping of Subgroup and Soil Family Classes with Data Mining Algorithms. Water and Soil journal, 34(4), 973-987. (In Farsi). Noroozi, A. A., Homaee, M., & ABBASI, F. (2011). Integrated application of remote sensing and spatial statistical models to the identification of soil salinity: A case study from Garmsar Plain, Iran. Journal of Environmental Sciences. (In Farsi). Parsaie, F., Firouzi, A. F., Mousavi, S. R., Rahmani, A., Sedri, M. H., & Homaee, M. (2021). Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map. Environmental Monitoring and Assessment, 193(4), 1-15. Peng, J., Biswas, A., Jiang, Q., Zhao, R., Hu, J., Hu, B., & Shi, Z. (2019). Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337, 1309-1319. Quinlan, J. R. (1992). Learning with continuous classes. In 5th Australian joint conference on artificial intelligence (Vol. 92, pp. 343-348). Rahmani, A., Sarmadian, F., Mousavi, S. R., & Khamoshi, S. E. (2020). Application of Geomorphometric attributes in digital soil mapping by using of machine learning and fuzzy logic approaches. Journal of Range and Watershed Management, 73(1), 105-124. (In Farsi). Schoeneberger, P.J., Wysocki, D.A. and Benham, E.C. (2012) Soil Survey Staff. Field book for describing and sampling soils, 3nd version. Natural Resources Conservation Service. National Soil Survey Center, Lincoln. Soil survey manual. (2018). Soil Science Division Staff. United States Department of Agriculture Handbook No. 18. Suleymanov, A., Abakumov, E., Suleymanov, R., Gabbasova, I., & Komissarov, M. (2021). The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes. ISPRS International Journal of Geo-Information, 10(4), 243. Sumfleth, K., & Duttmann, R. (2008). Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecological indicators, 8(5), 485-501. Taghizadeh-Mehrjardi, R., Ayoubi, S., Namazi, Z., Malone, B. P., Zolfaghari, A. A., & Sadrabadi, F. R. (2016). Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming. Arid Land Research and Management, 30(1), 49-64. Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., & Malone, B. P. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15-28. Taghizadeh-Mehrjardi, R., Schmidt, K., Toomanian, N., Heung, B., Behrens, T., Mosavi, A., ... & Scholten, T. (2021). Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models. Geoderma, 383, 114793. Tran, P. H., Nguyen, A. K., Liou, Y. A., Hoang, P. P., & Nguyen, H. T. (2018). Estimation of salinity intrusion by using Landsat 8 OLI data in The Mekong Delta, Vietnam. U.S. Geology Survey. (2014). Geology.com/news/2010/freelansatimages-from-USGS-2. http://glovis.usgs.gov. Van Wambeke, A. R. (2000). The Newhall Simulation Model for estimating soil moisture and temperature regimes. Department of Crop and Soil Sciences. Cornell University, Ithaca, NY. USA. Wang, J., Ding, J., Yu, D., Teng, D., He, B., Chen, X., ... & Su, F. (2020). Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI. Science of the Total Environment, 707, 136092. Wilding, L. P. (1985). Spatial variability: its documentation, accommodation and implication to soil surveys. In Soil spatial variability, Las Vegas NV, 30 November-1 December 1984 (pp. 166-194). Zeraatpisheh, M., Jafari, A., Bodaghabadi, M. B., Ayoubi, S., Taghizadeh-Mehrjardi, R., Toomanian, N., ... & Xu, M. (2020). Conventional and digital soil mapping in Iran: Past, present, and future. Catena, 188, 104424. Zinck, J. A., Metternicht, G., Bocco, G., & Del Valle, H. F. (2016). Geopedology an Integration of Geomorphology and Pedology for Soil and Landscape Studies. Ed.
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آمار تعداد مشاهده مقاله: 689 تعداد دریافت فایل اصل مقاله: 441 |
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