ایجاد توابع انتقالی شبه‌پیوسته برای برآورد منحنی نگه‌داشت آب خاک با استفاده از روش درخت M5

 1.Yuan, S., Liu, X., & Buzzi, O. (2021). A microstructural perspective on soil collapse. Geotechnique, 71(2), 132-140. doi: 10.1680/jgeot.18.P.256.

2.Rawls, W. J., Brakensiek, C. L., & Saxton, K. E. (1982). Estimation of soil water properties. Transactions - American Society of Agricultural Engineers, 25(5), 1316-1320. doi: 10.13031/2013. 33720.

3.Rawls, W. J., Nemes, A., & Pachepsky, Y. (2004). Effect of soil organic carbon on soil hydraulic properties. p. 95-114, In:
Y. Pachepsky & W.J. Rawls, Editors. Developments in Soil Science, Elsevier. doi: https://doi.org/10.1016/S0166-2481 (04)30006-1.

4.Kuzmanovski, V., Trajanov, A., Leprince, F., Džeroski, S., & Debeljak, M. (2015). Modeling water outflow from tile-drained agricultural fields. Science of The Total Environment, 505, 390-401. doi: https:// doi.org/10.1016/j.scitotenv.2014.10.009.

5.Vereecken, H., Amelung, W., Bauke,
S. L., Bogena, H., Brüggemann, N., Montzka, C., Vanderborght, J., Bechtold, M., Blöschl, G., Carminati, A., Javaux, M., Konings, A. G., Kusche, J., Neuweiler, I., Or, D., Steele-Dunne, S., Verhoef, A., Young, M., & Zhang, Y. (2022). Soil hydrology in the Earth system. Nature Reviews Earth & Environment, 3(9), 573-587. doi: 10. 1038/s43017-022-00324-6.

6.Gupta, S., & Larson, W. (1979). Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density. Water Resources Research, 15(6), 1633-1635. doi: https://doi.org/ 10.1029/WR015i006p01633.

7.Vereecken, H., Weynants, M., Javaux, M., Pachepsky, Y., Schaap, M., & Genuchten, M. T. (2010). Using pedotransfer functions to estimate the van Genuchten–Mualem soil hydraulic properties: A review. Vadose Zone Journal, 9(4), 795-820. doi: https:// doi.org/10.2136/vzj2010.0045.

8.Zhang, Y., & Schaap, M. G. (2017). Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology,
547, 39-53. doi: https://doi.org/10.1016/ j.jhydrol.2017.01.004.

9.Weihermüller, L., Lehmann, P., Herbst, M., Rahmati, M., Verhoef, A., Or, D., Jacques, D., & Vereecken, H. (2021). Choice of Pedotransfer Functions Matters when Simulating Soil Water Balance Fluxes. Journal of Advances in Modeling Earth Systems, 13(3), e2020MS002404. doi:https://doi.org/10.1029/2020MS002404.

10.Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892-898. doi: https:// doi.org/10.2136/sssaj1980.03615995004400050002x.

11.Wösten, J., Pachepsky, Y. A., & Rawls, W. (2001). Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251(3), 123-150. doi: https://doi.org/ 10.1016/S0022-1694(01)00464-4.

12.Minasny, B., & McBratney, A. B. (2002). The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66(2), 352-361. doi: https://doi.org/10.2136/sssaj2002.3520.

13.Haghverdi, A., Cornelis, W. M., & Ghahraman, B. (2012). A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data. Journal of Hydrology, 442-443, 46-54. doi: https:// doi.org/10.1016/j.jhydrol.2012.03.036.

14.Haghverdi, A., Öztürk, H. S., & Cornelis, W. M. (2014). Revisiting the pseudo continuous pedotransfer function concept: Impact of data quality and
data mining method. Geoderma,
226-227, 31-38. doi: https://doi.org/ 10.1016/j.geoderma.2014.02.026.

15.Li, Y., & Vanapalli, S. K. (2022). Prediction of soil-water characteristic curves using two artificial intelligence (AI) models and AI aid design method for sands. Canadian Geotechnical Journal, 59(1), 129-143. doi: 10.1139/ cgj-2020-0562.

16.Rastgou, M., Bayat, H., Mansoorizadeh, M., & Gregory, A. S. (2022). Estimating Soil Water Retention Curve by Extreme Learning Machine, Radial Basis Function, M5 Tree and Modified Group Method
of Data Handling Approaches. Water Resources Research, 58(4), e2021WR 031059. doi: https://doi.org/10.1029/ 2021WR031059.

17.Haghverdi, A., Öztürk, H. S., & Durner, W. (2018). Measurement and estimation of the soil water retention curve using the evaporation method and the pseudo continuous pedotransfer function. Journal of Hydrology, 563, 251-259. doi: https://doi.org/10.1016/ j.jhydrol. 2018.06.007.

18.Nguyen, P. M., Haghverdi, A., de Pue, J., Botula, Y. D., Le, K. V., Waegeman, W., & Cornelis, W. M. (2017). Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils. Biosystems Engineering, 153, 12-27. doi: https:// doi.org/10.1016/j.biosystemseng. 2016.10.013.

19.Pachepsky, Y. A., & Rawls, W. (2003). Soil structure and pedotransfer functions. European Journal of Soil Science,
54(3), 443-452. doi: https://doi.org/ 10.1046/j.1365-2389.2003. 00485.x.

20.Zakerinia, M., & Ghorbani, K. (2013). Feasibility of decision tree application (M5 model) for determining soil moisture characteristic curve from easily available soil parameters. Journal of Water and Soil Conservation,
20(5), 221-230. doi: 20.1001.1.23222 069.1392.20.5.14.0. [In Persian]

21.Pachepsky, Y., & Rawls, W. J. 2004. Development of pedotransfer functions in soil hydrology. Vol. 30. p, 512, Elsevier.

22.Rastgou, M. (2020). Comprehensive comparison of the methods of developing pedotransfer functions (PTFs) and development of new algorithms to predict soil water retention curve (SWRC) and soil hydraulic conductivity curve (SHCC). in Soil Science and Enguneering. 2020, Bu-Ali Sina University: Hamedan, Iran. p. 363. [In Persian]

23.Gee, G. W., & Or, D. (2002). Particle- Size Analysis. p, 225-295. In: Warren, A.D. (ed) Methods of Soil Analysis. Part 4. Physical Methods. Soil Science Society of America Inc, Madison.

24.Grossman, R., & Reinsch, T. (2002). Bulk Density and Linear Extensibility. p. 201-228, In: J.H. Dane, Topp, G.C., Editor. Methods of Soil Analysis: Part 4 Physical Methods, Soil Science Society of America Inc, Madison.

25.Grossman, R. B., & Reinsch, T. G. (2002). Water Retention and Storage.
p. 201–228., In: J.H. Dane, Topp, G.C., Editor. Methods of Soil Analysis. Part 4. Physical Methods, Soil Science Society of America, Madison.

26.Reynolds, W. D., & Elrick, D. E. (2002). Falling Head Soil Core (tank) Method. p. 809-812., In: A.D. Warren, Editor. Methods of Soil Analysis. Part 4. Physical Methods, Soil Science Society of America Inc, Madison.

27.Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29-38. doi: http://dx.doi.org/ 10.1097/00010694-193401000-00003.

28.Kemper, W., & Rosenau, R. (1986). Aggregate stability and size distribution. p. 425-442, In: A. Klute, Editor. Methods of Soil Analysis: Part 1 Physical and Mineralogical Methods, Soil Science Society of America, Inc. Madison. doi: 10.2136/sssabookser5.1. 2ed.

29.Jones, C. A. (1983). Effect of soil texture on critical bulk densities for root growth. Soil Science Society of America Journal, 47(6), 1208-1211. doi: https:// doi.org/10.2136/sssaj1983.03615995004700060029x.

30.Tietje, O., & Tapkenhinrichs, M. (1993). Evaluation of pedo-transfer functions. Soil Science Society of America Journal, 57(4), 1088-1095. doil: https://doi.org/ 10.2136/sssaj1993.03615995005700040035x.

31.Pal, M., Singh, N. K., & Tiwari, N. K. (2012). M5 Model Tree for Pier Scour Prediction Using Field Dataset, Kimberley Structural Consulting Engineers. Journal of Civil Engineering, 16(6), 1079-1084. doi: 10.1007/s12205-012-1472-1.

32.Wösten, J. H. M., Pachepsky, Y. A., & Rawls, W. J. (2001). Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251(3), 123-150. doi: https:// doi.org/10.1016/S0022-1694(01)00464-4.

33.Akaike, H. (1974). A new look at the statistical model identification. Automatic Control, IEEE Transactions on,
19(6), 716-723. doi: 10.1109/tac.1974. 1100705.

34.Hwang, S. I., Lee, K. P., Lee, D. S., & Powers, S. E. (2002). Models for estimating soil particle-size distributions. Soil Science Society of America Journal, 66(4), 1143–1150. doi: https://doi.org/ 10.2136/sssaj2002.1143.

35.Ghavami, M. S., Ayoubi, S., Mosaddeghi, M. R., & Naimi, S. (2023). Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale. Journal of Mountain Science, 20(10), 2975-2992. doi: 10.1007/s11629-023-8056-z.

36.Wilding, L. P., & Drees, L. R. (1983). Spatial variability and pedology. p. 83–116, In: L.P. Wilding, N.E. Smeck & G.F. Hall, Editors. Pedogenesis and soil taxonomy: Concepts and interactions, Elsevier, New York.

37.Brady, N. C., & Weil, R. R. 2010. Elements of the Nature and Properties of Soils. Pearson Educational International Upper Saddle River, NJ. doi: http:// lccn.loc.gov/2016008568.

38.Hillel, D. 1998. Environmental soil physics: Fundamentals, applications, and environmental considerations. Academic press. Waltham.

39.Nemes, A., Rawls, W. J., & Pachepsky, Y. A. (2005). Influence of organic matter on the estimation of saturated hydraulic conductivity. Soil Science Society of America Journal, 69(4), 1330-1337. doi: https://doi.org/ 10.2136/sssaj2004.0055.

40.Pachepsky, Y., Rawls, W., Gimenéz, D., & Watt, J. P. C. (1998). Use of soil penetration resistance and group method of data handling to improve soil water retention estimates. Soil and Tillage Research, 49(1-2), 117-126. doi: https:// doi.org/10.1016/S0167-1987(98)00168-8.

41.Brooks, R. H., & Corey, A. J. (1964). Hydraulic properties of porous media. Hydrol. Pap. 3. Colorado State Univ., Fort Collins.

42.Jain, S. K., Singh, V. P., & Van Genuchten, M. T. (2004). Analysis of soil water retention data using artificial neural networks. Journal of Hydrologic Engineering, 9(5), 415-420. doi: 10.1061/ ~ASCE!1084-0699~2004!9:5~415!.

43.Saxton, K., & Rawls, W. (2006). Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal, 70(5), 1569-1578. doi: https://doi.org/10.2136/sssaj2005.0117.

44.Rastgou, M., Bayat, H., Mansoorizadeh, M., & Gregory, A. S. (2020). Estimating the soil water retention curve: Comparison of multiple nonlinear regression approach and random forest data mining technique. Computers and Electronics in Agriculture, 174, 105502. doi:https://doi.org/10.1016/j.compag.
2020.105502.

45.Rawls, W., Pachepsky, Y. A., Ritchie, J., Sobecki, T., & Bloodworth, H. (2003). Effect of soil organic carbon on soil water retention. Geoderma, 116(1), 61-76.
doi: https://doi.org/10.1016/S0016-7061 (03)00094-6.

46.Pachepsky, Y. A., & Rawls, W. J. (2003). Soil structure and pedotransfer functions. European Journal of Soil Science, 54(3), 443-451. doi:10.1046/j. 1365-2389.2003.00485.x.

47.Schaap, M. G., Leij, F. J., & Van Genuchten, M. T. (2001). Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology, 251(3-4), 163-176. doi: https://doi.org/10.1016/S0022-1694 (01) 00466-8.

48.Wagner, B., Tarnawski, V., Wessolek, G., & Plagge, R. (1998). Suitability of models for the estimation of soil hydraulic parameters. Geoderma,
86(3), 229-239. doi: https://doi.org/ 10.1016/S0016-7061(98)00040-8.

49.Tajik, S., Ayoubi, S., & Zeraatpisheh, M. (2020). Digital mapping of soil organic carbon using ensemble learning model in Mollisols of Hyrcanian forests, northern Iran. Geoderma Regional,
20, e00256. doi:https://doi.org/10. 1016/j.geodrs.2020.e00256.

50.Kay, B. D., & VandenBygaart, A. J. (2002). Conservation tillage and depth stratification of porosity and soil organic matter. Soil and Tillage Research,
66(2), 107-118. doi:https://doi.org/ 10. 1016/S0167-1987(02)00019-3.

51.Minasny, B., Hopmans, J. W., Harter, T., Eching, S., Tuli, A., & Denton, M. (2004). Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data. Soil Science Society of America Journal, 68(2), 417-429. doi: https://doi.org/10. 2136/sssaj2004.4170.

52.Stock, O., & Downes, N. K. (2008). Effects of additions of organic matter on the penetration resistance of glacial till for the entire water tension range. Soil and Tillage Research, 99(2), 191-201. doi:https://doi.org/10.1016/j.still.2008.02.002.

53.Beare, M., Hendrix, P., & Coleman, D. (1994). Water-stable aggregates and organic matter fractions in conventional-and no-tillage soils. Soil Science Society of America Journal, 58(3), 777-786. doi: https://doi.org/10.2136/ sssaj1994. 03615995005800030020x.

54.Tomasella, J., Pachepsky, Y., Crestana, S., & Rawls, W. (2003). Comparison of two techniques to develop pedotransfer functions for water retention. Soil Science Society of America Journal, 67(4), 1085-1092. doi: https://doi.org/ 10.2136/sssaj2003.1085

55.Samadianfard, S., Nazemi, A. H., & Sadraddini, A. A. (2014). M5 model tree and gene expression programming based modeling of sandy soil water movement under surface drip irrigation. Agriculture Science Developments, 3, 178-190.

56.Huynh, H. (2015). Improving M5 Model Tree by Evolutionary Algorithm. Østfold University College, Halden, Norway. Master Thesis. uri:http://hdl.handle. net/11250/293858.

57.Kisi, O., Shiri, J., & Demir, V. (2017). Chapter 3 - Hydrological Time Series Forecasting Using Three Different Heuristic Regression Techniques. p. 45-65, In: P. Samui, S. Sekhar & V. E. Balas, Editors. Handbook of Neural Computation, Academic Press. doi: https://doi.org/ 10.1016/B978-0-12-811318-9.00003-X.

58.Melucci, M., & Pretto, L. (2007). PageRank: When order changes. In European Conference on Information Retrieval. Springer.