نقشه‌برداری رقومی مقاومت فروروی و برشی خاک با استفاده از الگوریتم‌های یادگیری ماشین در حوزه آبخیز کیلانه، استان کردستان

Abdollahi, S., Delavar, M. A., & Shekari, P. (2013). Spatial distribution mapping of Pb, Zn and Cd and soil pollution assessment in Anguran area of Zanjan province. Journal of Water and Soil, (6), 1410-1420. https://doi.org/10.22067/jsw.v0i0.19254. (In Persian).

Al-Adhadh, A. R., Abbas, B. J., & Ali, A. M. (2021, March). Factors Influencing the Shear Strength of Clays: A review. Materials Science and Engineering, 1st International Conference on Engineering Science and Technology (ICEST 2020) 23rd-24th December 2020, Samawah, Iraq.

Alhai, D. P., Syakur, S., & Basri, H. (2021). Ketahanan Penetrasi Tanah pada Penggunan Lahan Hortikultura di Saree Kabupaten Aceh Besar. Jurnal Ilmiah Mahasiswa Pertanian, 6(4), 680-690. https://doi.org/10.17969/jimfp.v6i4.18350.‏

Asghari, Sh., Hasanpour Kashani, M., Shahab Arkhazloo, H. (2024). Modeling Soil Penetration Resistance Using Regression, Artificial Neural Network and Gene Expression Programming. Journal of Water and Soil. 38(2) 269-283. https://doi.org/10.22067/jsw.2024.86792.1385. (In Persian).

Asghari, Sh., & Shahabi, M. (2019). Spatial variability of soil saturated hydraulic conductivity and penetration resistance in salt-affected lands around Lake Urmia. Water and Soil, 33(1), 103-116. https://doi.org/10.22067/jsw.v33i1.74411. (In Persian).

Asgari, N., Ayoubi, S., Demattê, J. A. M. & Dotto, A. C. (2020). Carbonates and organic matter in soils characterized by reflected energy from 350–25000 nm wavelength. Journal of Mountain Science, 17(7), 1636-1651. https://doi.org/10.1007/s11629-019-5789-9.

Asghari, Sh., Sheykhzadeh, G. R., & Shahabi, M. (2017). Geostatistical analysis of soil mechanical properties in Ardabil plain of Iran. Archives of Agronomy and Soil Science, 63(12), 1631-1643. https://doi.org/10.1080/03650340.2017.1296136.

Babaeian, E., Homaee, M., Vereecken, H., Montzka, C., Norouzi, A. A., van Genuchten, M. T. (2015). A comparative study of multiple approaches for predicting the soil–water retention curve: hyperspectral information vs. basic soil properties. Soil Science Society of America Journal, 79, 1043-8501. https://doi.org/10.2136/sssaj2014.09.0355.

Bachmann, J., Contreras, K., Hartge, K. H., & MacDonald, R. (2006). Comparison of soil strength data obtained in situ with penetrometer and with vane shear test. Soil and Tillage Research, 87(1), 112-118. https://doi.org/10.1016/j.still.2005.03.001.

Banaie, M. H. (1998). Soil moisture and temperature regimes map of Iran. Soil and Water Research Institute. Ministry of Agriculture, Tehran, Iran, 1sheet.

Besalatpour, A., Hajabbasi, M. A., Ayoubi, S., Afyuni, M., Jalalian, A. & Schulin, R. (2012). Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system. Soil science and plant nutrition, 58(2), 149-160. https://doi.org/10.1080/00380768.2012.661078.

Brevik, E. C., Cerdà, A., Mataix-Solera, J., Pereg, L., Quinton, J. N., Six, J., & Van Oost, K. (2015). The interdisciplinary nature of soil. Soil, 1(1), 117- 129. https://doi.org/10.5194/soil-1-117-2015, 2015.

Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324.

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, San Francisco, California. USA.

Davari, M., Karimi, S. A., Bahrami, H. A., Hossaini, S. M. A., Fahmideh, S. (2021). Simultaneous prediction of several soil properties related to engineering uses based on laboratory Vis-NIR reflectance spectroscopy. Catena. 197, 104987. https://doi.org/10.1016/j.catena.2020.104987.

Demir, S., & Şahin, E. K. (2021). Assessment of feature selection for liquefaction prediction based on recursive feature elimination. Avrupa Bilim ve Teknoloji Dergisi, (28), 290-294. https://doi.org/10.31590/ejosat.998033.

Deveci, E. R. D. E. M., Ozan, E., Kirpinar, I., Oral, M., DALOĞLU, A. G., Aydin, N., & ÖZTÜRK, A. (2013). Neurocognitive functioning in young high-risk offspring having a parent with bipolar I disorder. Turkish Journal of Medical Sciences, 43(1), 110-117. https://doi.org/10.3906/sag-1205-78.

De Baets, S., Poesen, J., Gyssels, G., & Knapen, A. (2009). Effects of grass roots on the erodibility of topsoils during concentrated flow. Geomorphology, 76(1-2), 54-67. https://doi.org/10.1016/j.geomorph.2005.10.002.

Ekwue, E. I., & Stone, R. J. (1995). Organic matter effects on the strength properties of compacted agricultural soils. Transactions of the ASAE, 38(2), 357-365.

Fan C. C. & Su C. F. (2008). Role of roots in shear strength of root-reinforced soils and with high moisture content. Ecological Engineering. 33, 157–166. https://doi.org/10.1016/j.ecoleng.2008.02.013.

Ferreira, F. B., Vieira, C. S., & Lopes, M. D. L. (2015). Direct shear behaviour of residual soil–geosynthetic interfaces–influence of soil moisture content, soil density and geosynthetic type. Geosynthetics International, 22(3), 257-272. https://doi.org/10.1680/gein.15.00011.

Franti, T. G., Laflen, J. M. & Watson, D. A. (1985). Soil erodibility and critical shear under concentrated flow. American Society of Agricultural Engineers. 19, 85-2033.

Gao, X. S., Yi, X. I. A. O., Deng, L. J., Li, Q. Q., Wang, C. Q., Bing, L. I., & Min, Z .E .N .G. (2019). Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin. China. Journal of Integrative Agriculture, 18(2), 279–289. https://doi.org/10.1016/S2095-3119(18)62069-6.

Gomez, C., Philippe, L., & Guillaume, C. (2008). Continuum moval versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma. 148, 14148. https://doi.org/10.1016/j.geoderma.2008.09.016.

Havaee, Sh. Ayoubi, S. Mosaddeghi, M. R. (2014). Surface Shear Strength Modeling Using Soil and Environmental Attributes in Landscape Scale (Semirom District, Isfahan Province). Journal of Water and Soil. 28, 319-329. https://doi.org/10.1016/j.geoderma.2008.09.016. (In Persian).

Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink. G .B .M. (2018).  Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518. https://doi.org/10.7287/peerj.preprints.26693v3.

Hongde, W., Dongli, S., Yipeng, Z., & Donghao, M. (2022). Unsaturated soil shear strength of agricultural soils influenced by reclamation sequences in coastal China. European Journal of Soil Science, 73(3), e13237. https://doi.org/10.1111/ejss.13237.

Horn, R., & Smucker, A. J. M. (2005). Soil Compaction and Its Management. In Soil Physical Measurement and Interpretation for Land Evaluation. 125-146.

Janik, L. J., Merry, R. H. Forrester, S. T. Lanyon D. M. Rawson. A. (2009). Rapid prediction of soil water retention using mid infrared spectroscopy. Soil Science Society of America Journal, 71(2), 507-514. https://doi.org/10.2136/sssaj2005.0391.

Jakšić, S., Ninkov, J., Milić, S., Vasin, J., Živanov, M., Perović, V. & Komlen, V. (2021). Topographic position, land use and soil management effects on soil organic carbon (vineyard region of Niš, Serbia). Agronomy, 11(7), 1438. https://doi.org/10.3390/agronomy11071438.

Jiang, Q., Cao, M., Wang, Y., Wang, J., & He, Z. (2021). Estimation of soil shear strength indicators using soil physical properties of paddy soils in the plastic state. Applied Sciences, 11(12), 5609. https://doi.org/10.3390/app11125609.

Kang, J., Wei, J., Gan, F., & Li, J. (2021). Shear Strength of Purple Topsoil under Different Land Uses in the Three Gorges Reservoir Area, China. Mountain Research and Development, 41(3), R1. https://doi.org/10.1659/MRD-JOURNAL-D-20-00081.1.

Karamooz, A., & Araghinejad, S. H. (2005). Advanced hydrology, Industrial University of Amir Kabir (Poly Technics). Iran, Publication Centre of Amir Kabir University, Tehran.

Khaledian, Y., & Miller, B. A. (2020). Selecting appropriate machine learning methods fordigital soil mapping. Appl. Math Model. 81, 401–418. https://doi.org/10.1016/j.apm.2019.12.016.

Khayamim, F., Wetterlind, J., Khademi, H., Robertson, A. J., Cano, A. F. & Stenberg, B. (2015). Using visible and near infrared spectroscopy to estimate carbonates and gypsum in soils in arid and subhumid regions of Isfahan, Iran. Journal of Near Infrared Spectroscopy. 23(3), 155-165. https://api.semanticscholar.org/CorpusID:15430731.

Khalil, M.B., Afyuni, M., Jalalian, A., Abbaspour, K.C., & Dehghani, A. A. (2011). Estimation surface soil shear strength by pedo-transfer functions and soil spatial prediction functions. Water and Soil (Agricultural Sciences and Technology), 187–195. https://doi.org/10.22067/jsw.v0i0.8520. (In Persian).

Kunakh, O., Zhukova, Y., Yakovenko, V., & Daniuk, O. (2022). Influence of plants on the spatial variability of soil penetration resistance. Ekológia (Bratislava), 41(2), 113-125. https://doi.org/10.2478/eko-2022-0012.

Khosravani, P., Moosavi, A., Baghernejad, M. (2021). Spatial Variations of Soil Penetration Resistance and Shear Strength and the Effect of Land Use Type and Physiographic Unit on These Characteristics. Iranian journal of Soil and water research. 52 (4), 1041-1057. (In Persian).

Khosravani, P., Baghernejad, M., Moosavi, A. A. & Rezaei, M. (2023). Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran. Environmental Monitoring and Assessment, 195(11), 1367. https://doi.org/10.1007/s10661-023-11980-6.

Liu, X., Cheng, X., Wang, N., Meng, M., Jia, Z., Wang, J. & Zhang, J. (2021). Effects of vegetation type on soil shear strength in Fengyang Mountain Nature Reserve, China. Forests, 12(4), 490. https://doi.org/10.3390/f12040490.

Leónard, J., & Richard, G. (2004). Estimation of runoff critical shear stress for soil erosion from soil shear strength. Catena. 57: 233–249. https://www.researchgate.net/publication/223891102.

Li, H., Zhang, J., Yang, X., Ye, M., Jiang, W., Gong, J. & Xu, Z. (2024). Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement. Construction and Building Materials, 434, 136675. https://doi.org/10.1016/j.conbuildmat.2024.136675.

Machado, T. D. A., Mendes, Í. N., Moraes, E. R. D., & Sousa, E. D. T. D. S. (2023). Modification of soil physical atributes as a function of subsoiling operations under different managements. Revista Brasileira de Engenharia Agrícola e Ambiental, 27(4), 293-299. https://doi.org/10.1590/1807-1929/agriambi.v27n4p293-299.

Mahmoudzadeh, H., Matinfar, H. R., Taghizadeh-Mehrjardi, R., & Kerry, R. (2020). Spatial prediction of soil organic carbon using machine learning techniques in western Iran. Geoderma Regional, 21, e00260. https://doi.org/10.1016/j.geodrs.2020.e00260.

Matthew, W. (2011). Bias of the random forest out-of-bag (OOB) error for certain input parameters. Open Journal of Statistics, 2011. https://www.researchgate.net/publication/275999921.

Mendes, W. D. S., Demattê, J. A. M., Barros, A. S .E., Salazar, D. F. U., & Amorim, M. T. A. (2020). Geostatistics or machine learning for mapping soil attributes and agricultural practices. Revista Ceres, 67(4), 330-336. https://doi.org/10.1590/0034-737X202067040010.

Minasny, B., & McBratney, A. B. (2016). Digital soil mapping: A brief history and some lessons. Geoderma. 264, 301-311. https://doi.org/10.1016/j.geoderma.2015.07.017.

Mousavi, S., Sarmadian, F., Omid, M., & Bogaert, P. (2021^a). Modeling the Vertical Soil Calcium Carbonate Equivalent Variation by Machine Learning Algorithms in Qazvin Plain. Water and Soil, 35(5), 719-734. https://doi.org/10.22067/jsw.2021.71748.1076.

Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2021^b). Digital Modeling of Three-Dimensional Soil Salinity Variation Using Machine Learning Algorithms in Arid and Semi-Arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 52(7), 1915-1929. https://doi.org/10.22059/ijswr.2021.323030.668957. (In Persian).

Mousavi, S. R., Sarmadian, F., Omid. M. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran, Measurement. https://doi.org/10.1016/j.measurement.2022.111706.

Momeni, E., He, B., Abdi, Y., & Armaghani, D. J. (2023). Novel Hybrid XGBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests. CMES-Computer Modeling in Engineering & Sciences, 136(3). https://doi.org/10.32604/cmes.2023.026531.

Monroy-Rodríguez, F. L., Álvarez-Herrera, J. G., & Alvarado-Sanabria, Ó. H. (2017). Distribución espacial de algunas propiedades físicas del suelo en un transecto de la granja Tunguavita, Paipa. Revista UDCA Actualidad & Divulgación Científica, 20(1), 91-100. https://doi.org/10.31910/rudca.v20.n1.2017.66.

Nazari, R., Ramezani Etedali, H., Nazari, B., & Collins, B. (2020). The impact of climate variability on water footprint components of rainfed wheat and barley in the Qazvin province of Iran. Irrigation and Drainage, 69(4), 826-843. https://doi.org/10.1002/ird.2487.

Nguyen, T. T, (2021). Predicting agricultural soil carbon using machine learning. Nature Reviews Earth & Environment, 2(12), 825-825. https://doi.org/10.1038/s43017-021-00243-y.

Nguyen, T.T., Tuyen, T.T., Sarzhanovna, A.T., Thuy, H. T., Luong, V. V., Du, T. D., & Khanh, V. T. (2023). Potential risks of soil erosion in North-Central Vietnam using remote sensing and GIS. Revista Brasileira de Engenharia Agrícola e Ambiental, 27(11), 910-916. https://doi.org/10.1590/1807-1929/agriambi.v27n11p910-916.

Ohu, J. O., Raghavan, G. S. V., McKyes, E., & Mehuys, G. (1986). Shear strength prediction of compacted soils with varying added organic matter contents. Transactions of the ASAE, 29(2), 351-355. https://www.researchgate.net/publication/270613321.

Ozlu, E., Arriaga, F. J., Bilen, S., Gozukara, G., & Babur, E. (2022). Carbon footprint management by agricultural practices. Biology, 11(10), 1453. https://doi.org/10.3390/biology11101453.

Padarian, J.,  Minasny, B.,  McBratney, A.B. (2020). Machine learning and soil sciences: a review aided by machine learning tools. Soil, 6(1), 35-52. https://doi.org/10.5194/soil-6-35-2020, 2020.

Parsaie, F., Farrokhian Firouzi, A., 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, 1-15. https://doi.org/10.1007/s10661-021-08947-w.

Peele, T. C. (1938). The relation of certain physical characteristics to the erodibility of soils. Soil Science Society of America, 2, 97-100. https://doi.org/10.2136/sssaj1938.036159950002000C0015x.

Qin, N., Wang, K., Sun, H., Wang, D., & Wang, B. (2023). Forecasting the Mechanical Compaction Influence on Soybean Yield Using XGBoost, Available at SSRN, 28 Pages. https://ssrn.com/abstract=4618322.

Rezaei, M., Mousavi, S. R., Rahmani, A., Zeraatpisheh, M., Rahmati, M., Pakparvar, M. & Cornelis, W. (2023). Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil. Computers and Electronics in Agriculture, 209, 107821. http://hdl.handle.net/1854/LU-01H7SSJSEZYS59DNXHB4FFF5R0.

Rezaei, M., & Tabatabai Klor R. (2019). Investigation of the effect of depth and moisture on soil shear strength in field and laboratory, 50 (2), 367-374.

Schneider, W. E., & Young, R. (1997). Spectroradiometry methods. Handbook of Applied Photometry, ed. Casimer De Cusatis, 252.

Shahabi, A., Nabiollahi, K., Davari, M., Zeraatpisheh, M., Heung, B., Scholten, T., Taghizadeh-Mehrjardi, R. (2022). Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates. Geocarto International. 37)27:( 18172–18195. http://dx.doi.org/10.1080/10106049.2022.2138565.

Simon, A., & Collison, A. J. C. (2002). Quantifying the mechanical and hydrologic effects of riparian vegetation on streambank stability. Earth Surface Processes and Landforms, 27(5), 527-546. https://doi.org/10.1002/esp.325.

Soil Science Society of America, (2008). Glossary of Soil Science Terms (pp. 18-77). Madison, Wis.

Soil Survey Staff, (2022). Keys to soil Taxonomy. In: U.S. Department of Agriculture, Natural Resources Conservation Service, thirteenth ed. 18-77. Washington, DC.

Santos, F.L., Jesus, V.A.M.D., & Valente, D.S.M. (2012). Modelagem da resistência à penetração do solo usando análises estatísticas e redes neurais artificiais. Acta Scientiarum. Agronomy, 34, 219-224. https://doi.org/10.4025/actasciagron.v34i2.11627.

Sun, Z., Liu, F., Wang, D., Wu, H., & Zhang, G. (2023). Improving 3D Digital Soil Mapping Based on Spatialized Lab Soil Spectral Information. Remote Sensing, 15(21), 5228. https://doi.org/10.3390/rs15215228.

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. https://doi.org/10.1016/j.geoderma.2013.07.020.

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, 1-21. https://doi.org/10.1016/j.geoderma.2020.114793.

Virgo, K. J., & Munro, R. N. (1978). Soil and erosion features of the Central Plateau region of Tigrai, Ethiopia. Geoderma, 20(2), 131-157. https://doi.org/10.1016/0016-7061(78)90040-X.

Viscarra Rossel, R .A .V. (2008). ParLeS: Software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems, 90, 72–83. https://doi.org/10.1016/j.chemolab.2007.06.006.

Wilding, L. P., & Dress, L. R. (1983). In Application of geostatistics to spatial studies of soil. Eds. BB Trangmar, RS Yost and G Uehara. Advances in Agr, 38.

Zeitfogel, H., Feigl, M., Schulz, K. (2022). Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity. Geoderma, 433, 116418. https://doi.org/10.1016/j.geoderma.2023.116418.

Zhang, J., Wang, J., Zhao, N., Shi, J., & Wang, Y. (2024). Analysis of Changes in Runoff and Sediment Load and Their Attribution in the Kuye River Basin of the Middle Yellow River Based on the Slope Change Ratio of Cumulative Quantity Method. Water, 16(7), 944. https://doi.org/10.3390/w16070944.

Zhang, Y., Lu, J., Han, W., Xiong, Y., & Qian, J. (2023). Effects of moisture and stone content on the shear strength characteristics of soil-rock mixture. Materials, 16(2), 567. https://doi.org/10.3390/ma16020567.

Zhang, X., Xue, J., Chen, S., Wang, N., Shi, Z., Huang, Y., & Zhuo, Z. (2022). Digital mapping of soil organic carbon with machine learning in dryland of Northeast and North plain China. Remote Sensing, 14(10), 2504. https://doi.org/10.3390/rs14102504.

Zhang, Y., Wang, F., Zhang, J., Zhu, T., Lin, C., Müller, C., & Cai, Z. (2015). Cattle manure and straw have contrasting effects on organic nitrogen mineralization pathways in a subtropical paddy soil. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 65(7), 619-628. https://doi.org/10.1080/09064710.2015.1039054.

Zhao, S., Ayoubi, S., Mousavi, S. R., Mireei, S. A., Shahpouri, F., Wu, S. X., ... & Tian, C. Y. (2024). Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China. Journal of Environmental Management, 364, 121311. https://doi.org/10.1016/j.jenvman.2024.121311.

Zhu, L., Liao, Q., Wang, Z., Chen, J., Chen, Z., Bian, Q., & Zhang, Q. (2022). Prediction of soil shear Strength parameters using combined data and different machine learning models. Applied Sciences, 12(10), 5100. https://doi.org/10.3390/app12105100.