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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許少瑜 | zh_TW |
dc.contributor.advisor | Shao-Yiu Hsu | en |
dc.contributor.author | 馮博煜 | zh_TW |
dc.contributor.author | Bo-Yu Fung | en |
dc.date.accessioned | 2023-10-03T16:22:51Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-10-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-12 | - |
dc.identifier.citation | Abdelbaki, A. M. (2020), Assessing the best performing pedotransfer functions for predicting the soil‐water characteristic curve according to soil texture classes and matric potentials, European Journal of Soil Science, 72(1), 154-173. https://doi.org/10.1111/ejss.12959
Ahuja, L., J. Naney, R. Williams (1985), Estimating soil water characteristics from simpler properties or limited data, Soil Science Society of America Journal, 49(5), 1100-1105. https://doi.org/10.2136/sssaj1985.03615995004900050005x Ahuja, L. R., D. Swartzendruber (1972), An improved form of soil‐water diffusivity function, Soil Science Society of America Journal, 36(1), 9-14. https://doi.org/10.2136/sssaj1972.03615995003600010002x Aina, P., S. Periaswamy (1985), Estimating available water-holding capacity of western Nigerian soils from soil texture and bulk density, using core and sieved samples, Soil Science, 140(1), 55-58. https://doi.org/10.1097/00010694-198507000-00007 Akinwande, M. O., H. G. Dikko, A. Samson (2015), Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis, Open journal of statistics, 5(07), 754. https://doi.org/10.4236/ojs.2015.57075 Allison, P. D. (1999), Multiple regression: A primer, Pine Forge Press. Amorim, R. S. S., J. A. Albuquerque, E. G. Couto, M. Kunz, M. F. Rodrigues, L. d. C. M. da Silva, J. M. Reichert (2022), Water retention and availability in Brazilian Cerrado (neotropical savanna) soils under agricultural use: Pedotransfer functions and decision trees, Soil and Tillage Research, 224, 105485. https://doi.org/10.1016/j.still.2022.105485 Arya, L., T. S. Dierolf (1992), Predicting soil moisture characteristics from particle-size distributions: An improved method to calculate pore radii from particle radii, paper presented at Proceedings of the International Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils, University of California Press Riverside, CA. Arya, L. M., J. F. Paris (1981), A physicoempirical model to predict the soil moisture characteristic from particle‐size distribution and bulk density data, Soil Science Society of America Journal, 45(6), 1023-1030. https://doi.org/10.2136/sssaj1981.03615995004500060004x Baker, L., D. Ellison (2008), Optimisation of pedotransfer functions using an artificial neural network ensemble method, Geoderma, 144(1-2), 212-224. https://doi.org/10.1016/j.geoderma.2007.11.016 Banin, A., A. Amiel (1970), A correlative study of the chemical and physical properties of a group of natural soils of Israel, Geoderma, 3(3), 185-198. https://doi.org/10.1016/0016-7061(70)90018-2 Berndt, R., K. Coughlan (1977), The nature of changes in bulk density with water content in a cracking clay, Soil Research, 15(1), 27-37. https://doi.org/10.1071/SR9770027 Berry, W. D., W. D. Berry, S. Feldman, D. Stanley Feldman (1985), Multiple regression in practice, Sage. https://doi.org/10.4135/9781412985208 Bloemen, G. W. (1980), Calculation of Hydraulic Conductivities of Soils from Texture and Organic matter Content, Zeitschrift für Pflanzenernährung und Bodenkunde, 143(5), 581-605. https://doi.org/10.1002/jpln.19801430513 Bouma, J. (1989), Using soil survey data for quantitative land evaluation, Advances in soil science, 9(1989), 177-213. https://doi.org/10.1007/978-1-4612-3532-3_4 Breiman, L. (1984), Classification and regression trees, Routledge. https://doi.org/10.2307/2530946 Breiman, L. (2001), Random forests, Machine learning, 45, 5-32. Brooks, R., A. Corey (1964), Hydraulic properties of porous media. Hydrology Paper No. 3, Civil Engineering Department, Colorado State University, Fort Collins, CO. Bruand, A., O. Duval, H. Gaillard, R. Darthout, M. Jamagne (1996), Variabilité des propriétés de rétention en eau des sols: importance de la densité apparente, Etude et Gestion des sols, 31(1), (1) 27-40. Campbell, G., S. Shiozawa (1992), Prediction of hydraulic properties of soils using particle-size distribution and bulk density data, Indirect methods for estimating the hydraulic properties of unsaturated soils, 317-328. Cassel, D., D. Nielsen (1986), Field capacity and available water capacity, Methods of soil analysis: part 1 physical and mineralogical methods, 5, 901-926. https://doi.org/10.2136/sssabookser5.1.2ed.c36 Christiaens, K., J. Feyen (2001), Analysis of uncertainties associated with different methods to determine soil hydraulic properties and their propagation in the distributed hydrological MIKE SHE model, Journal of hydrology, 246(1-4), 63-81. https://doi.org/10.1016/S0022-1694(01)00345-6 Clapp, R. B., G. M. Hornberger (1978), Empirical equations for some soil hydraulic properties, Water resources research, 14(4), 601-604. https://doi.org/10.1029/WR014i004p00601 Colman, E. (1947), A laboratory procdure for determining the field capacity of soils, Soil Science, 63(4), 277-284. https://doi.org/10.1097/00010694-194704000-00003 Cornelis, W. M., J. Ronsyn, M. Van Meirvenne, R. Hartmann (2001), Evaluation of pedotransfer functions for predicting the soil moisture retention curve, Soil Science Society of America Journal, 65(3), 638-648. https://doi.org/10.2136/sssaj2001.653638x Costa, A. d., J. A. Albuquerque, J. A. d. Almeida, A. d. Costa, R. V. Luciano (2013), Pedotransfer functions to estimate retention and availability of water in soils of the state of Santa Catarina, Brazil, Revista Brasileira de Ciência do Solo, 37, 889-910. https://doi.org/10.1590/S0100-06832013000400007 Craney, T. A., J. G. Surles (2002), Model-dependent variance inflation factor cutoff values, Quality engineering, 14(3), 391-403. https://doi.org/10.1081/QEN-120001878 Dashtaki, S. G., M. Homaee, H. Khodaverdiloo (2010), Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data, Soil use and management, 26(1), 68-74. https://doi.org/10.1111/j.1475-2743.2009.00254.x Davies, B. E. (1974), Loss‐on‐ignition as an estimate of soil organic matter, Soil Science Society of America Journal, 38(1), 150-151. https://doi.org/10.2136/sssaj1974.03615995003800010046x Davison, A. C., D. V. Hinkley (1997), Bootstrap methods and their application, Cambridge university press. https://doi.org/10.1017/CBO9780511802843 Dexter, A. (2004), Soil physical quality: Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth, Geoderma, 120(3-4), 201-214. https://doi.org/10.1016/j.geoderma.2003.09.004 Division of Soil Survey (1993), Soil survey manual, US Department of Agriculture. Draper, N. R., H. Smith (1998), Applied regression analysis, John Wiley & Sons. Dybowski, R., S. J. Roberts (2001), Confidence intervals and prediction intervals for feed-forward neural networks, edited, Cambridge University Press. https://doi.org/10.1017/CBO9780511543494.013 Efron, B., R. J. Tibshirani (1994), An introduction to the bootstrap, CRC press. Eiter, T., H. Mannila (1994), Computing discrete Fréchet distance. El-Kadi, A. I. (1985), On estimating the hydraulic properties of soil, Part 2. A new empirical equation for estimating hydraulic conductivity for sands, Advances in water resources, 8(3), 148-153. https://doi.org/10.1016/0309-1708(85)90055-7 Endelman, F. J., G. E. Box, J. R. Boyle, R. R. Hughes, D. R. Keeney, M. L. Northup, P. G. Saffigna (1974), Mathematical modeling of soil--water--nitrogen phenomena, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). F.E. Allison (1973), Developments in Soil Science, Elsevier. https://doi.org/https://doi.org/10.1016/S0166-2481(08)70587-7 Fisher, A., C. Rudin, F. Dominici (2018), All models are wrong but many are useful: variable importance for black-box, proprietary, or misspecified prediction models, using model class reliance, arXiv preprint arXiv:1801.01489, 1. Fooladmand, H. R. (2011), Pedotransfer functions for point estimation of soil moisture characteristic curve in some Iranian soils, African Journal of Agricultural Research, 6(6), 1586-1591. Fréchet, M. M. (1906), Sur quelques points du calcul fonctionnel, Rendiconti del Circolo Matematico di Palermo (1884-1940), 22(1), 1-72. Ghanbarian-Alavijeh, B., H. Millán (2009), The relationship between surface fractal dimension and soil water content at permanent wilting point, Geoderma, 151(3-4), 224-232. https://doi.org/10.1016/j.geoderma.2009.04.014 Grismer, M. (1987), Water vapor adsorption and specific surface, Soil Science, 144(3), 233-236. https://doi.org/10.1097/00010694-198709000-00010 Guber, A. K., Y. A. Pachepsky (2010), Multimodeling with pedotransfer functions: Documentation and user manual for PTF Calculator (CalcPTF), version 3.0, USDA Rep., Beltsville Agricultural Research Center, Beltsville, Md, 26. Gupta, S., W. Larson (1979), Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density, Water resources research, 15(6), 1633-1635. https://doi.org/10.1029/WR015i006p01633 Hall, D., M. Reeve, A. Thomasson, V. Wright (1977), Water retention, porosity and density of field soils. Hastie, T., R. Tibshirani, J. H. Friedman, J. H. Friedman (2009), The elements of statistical learning: data mining, inference, and prediction, Springer. Haverkamp, R., M. Vauclin, J. Touma, P. Wierenga, G. Vachaud (1977), A comparison of numerical simulation models for one‐dimensional infiltration, Soil Science Society of America Journal, 41(2), 285-294. https://doi.org/ 10.2136/sssaj1977.03615995004100020024x Hecht-Nielsen, R. (1989), Neurocomputing, Addison-Wesley Longman Publishing Co., Inc. Hesterberg, T. (2011), Bootstrap, Wiley Interdisciplinary Reviews: Computational Statistics, 3(6), 497-526. https://doi.org/10.1002/wics.182 Ho, T. K. (1995), Random decision forests, paper presented at Proceedings of 3rd international conference on document analysis and recognition, IEEE. Iman, R. L. (1992), Uncertainty and sensitivity analysis for computer modeling applications, ASME AEROSP DIV PUBL AD., ASME, NEW YORK, NY(USA), 1992, 28, 153-168. Ishwaran, H. (2015), The effect of splitting on random forests, Machine learning, 99, 75-118. https://doi.org/10.1007/s10994-014-5451-2 Jamison, V., E. Kroth (1958), Available moisture storage capacity in relation to textural composition and organic matter content of several Missouri soils, Soil Science Society of America Journal, 22(3), 189-192. https://doi.org/10.2136/sssaj1958.03615995002200030001x Jaynes, D. B., E. J. Tyler (1984), Using soil physical properties to estimate hydraulic conductivity, Soil Science, 138(4), 298-305. https://doi.org/10.1097/00010694-198410000-00007 Khosravi, A., S. Nahavandi, D. Creighton, A. F. Atiya (2011), Comprehensive review of neural network-based prediction intervals and new advances, IEEE Transactions on neural networks, 22(9), 1341-1356. https://doi.org/10.1109/TNN.2011.2162110 Kilmer, V. J., L. T. Alexander (1949), Methods of making mechanical analyses of soils, Soil Science, 68(1), 15-24. https://doi.org/10.1097/00010694-194907000-00003 Kim, J., T. Kawai, M. Kazama (2019), Minimum void ratio characteristic of soils containing non-plastic fines, Soils and Foundations, 59(6), 1772-1786. https://doi.org/10.1016/j.sandf.2019.08.001 Koekkoek, E., H. Booltink (1999), Neural network models to predict soil water retention, European Journal of Soil Science, 50(3), 489-495. https://doi.org/10.1046/j.1365-2389.1999.00247.x Kohler, M., A. Krzyżak, H. Walk (2009), Optimal global rates of convergence for nonparametric regression with unbounded data, Journal of Statistical Planning and Inference, 139(4), 1286-1296. https://doi.org/10.1016/j.jspi.2008.07.012 Kumar, S., A. N. Srivistava (2012), Bootstrap prediction intervals in non-parametric regression with applications to anomaly detection, paper presented at The 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. LeCun, Y., Y. Bengio (1995), Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks, 3361(10), 1995. LeCun, Y., B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel (1989), Backpropagation applied to handwritten zip code recognition, Neural computation, 1(4), 541-551. https://doi.org/10.1162/neco.1989.1.4.541 Li, Y., D. Chen, R. White, A. Zhu, J. Zhang (2007), Estimating soil hydraulic properties of Fengqiu County soils in the North China Plain using pedo-transfer functions, Geoderma, 138(3-4), 261-271. https://doi.org/10.1016/j.geoderma.2006.11.018 Lin, H., K. McInnes, L. Wilding, C. Hallmark (1999), Effects of soil morphology on hydraulic properties I. Quantification of soil morphology, Soil Science Society of America Journal, 63(4), 948-954. https://doi.org/10.2136/sssaj1999.634948x Liu, J., Z. Wang, F. Hu, C. Xu, R. Ma, S. Zhao (2020), Soil organic matter and silt contents determine soil particle surface electrochemical properties across a long-term natural restoration grassland, Catena, 190, 104526. https://doi.org/10.1016/j.catena.2020.104526 Manchuk, J., O. Leuangthong, C. Deutsch (2009), The proportional effect, Mathematical Geosciences, 41, 799-816. https://doi.org/10.1007/s11004-008-9195-z Maren, A. J., C. T. Harston, R. M. Pap (1990), Handbook of neural computing applications, Academic Press. https://doi.org/10.1016/C2013-0-11292-5 Matsuoka, H., T. Luo, Y. Yao (1999), Soil mechanics, Morikita Shuppan Co., Ltd, 61. McCarty, L. B., L. R. Hubbard, V. L. Quisenberry (2016), Applied soil physical properties, drainage, and irrigation strategies, Springer. https://doi.org/10.1007/978-3-319-24226-2 McCord-Nelson, M., W. T. Illingworth (1991), A practical guide to neural nets, Addison-Wesley Longman Publishing Co., Inc. McCuen, R., W. Rawls, D. Brakensiek (1981), Statistical analysis of the Brooks‐Corey and the Green‐Ampt parameters across soil textures, Water resources research, 17(4), 1005-1013. https://doi.org/10.1029/WR017i004p01005 McKenzie, N., D. Jacquier (1997), Improving the field estimation of saturated hydraulic conductivity in soil survey, Soil Research, 35(4), 803-827. https://doi.org/10.1071/S96093 Minasny, B., A. Mc Bratney (2002), Uncertainty analysis for pedotransfer functions, European Journal of Soil Science, 53(3), 417-429. https://doi.org/10.1046/j.1365-2389.2002.00452.x Minasny, B., A. B. McBratney, K. L. Bristow (1999), Comparison of different approaches to the development of pedotransfer functions for water-retention curves, Geoderma, 93(3-4), 225-253. https://doi.org/10.1016/S0016-7061(99)00061-0 Mualem, Y. (1976), A new model for predicting the hydraulic conductivity of unsaturated porous media, Water resources research, 12(3), 513-522. https://doi.org/10.1029/WR012i003p00513 Mullins, C. E., K. Smith, C. Mullins (2000), Matric potential, Soil and environmental analysis: Physical methods. eds ka smith and ce mullins, 65-93. Nemes, A., M. Schaap, J. Wösten (2003), Functional evaluation of pedotransfer functions derived from different scales of data collection, Soil Science Society of America Journal, 67(4), 1093-1102. https://doi.org/10.2136/sssaj2003.1093 Or, D., J. M. Wraith, A. Warrick (2002), Soil water content and water potential relationships, Soil physics companion, 1, 49-84. https://doi.org/10.1201/9781420041651-6 Pachepsky, Y., W. Rawls, D. Gimenez, J. Watt (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. https://doi.org/10.1016/S0167-1987(98)00168-8 Pachepsky, Y. A., D. Timlin, G. Varallyay (1996), Artificial neural networks to estimate soil water retention from easily measurable data, Soil Science Society of America Journal, 60(3), 727-733. https://doi.org/10.2136/sssaj1996.03615995006000030007x Patil, N., D. Pal, C. Mandal, D. Mandal (2012), Soil water retention characteristics of vertisols and pedotransfer functions based on nearest neighbor and neural networks approaches to estimate AWC, Journal of irrigation and drainage engineering, 138(2), 177-184. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000375 Patil, N. G., S. K. Singh (2016), Pedotransfer functions for estimating soil hydraulic properties: A review, Pedosphere, 26(4), 417-430. https://doi.org/10.1016/S1002-0160(15)60054-6 Petersen, L., P. Moldrup, O. Jacobsen, D. Rolston (1996), Relations between specific surface area and soil physical and chemical properties, Soil Science, 161(1), 9-21. https://doi.org/10.1097/00010694-199601000-00003 Puckett, W., J. Dane, B. Hajek (1985), Physical and mineralogical data to determine soil hydraulic properties, Soil Science Society of America Journal, 49(4), 831-836. https://doi.org/10.2136/sssaj1985.03615995004900040008x Rajkai, K., G. Várallyay (1992), Estimating soil water retention from simpler properties by regression techniques, paper presented at Proc. Int. Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils. University of California, Riverside. Rajkai, K., S. Kabos, M. T. Van Genuchten (2004), Estimating the water retention curve from soil properties: comparison of linear, nonlinear and concomitant variable methods, Soil and Tillage Research, 79(2), 145-152. https://doi.org/10.1016/j.still.2004.07.003 Rawls, W., D. Brakensiek, B. Soni (1983), Agricultural management effects on soil water processes part I: Soil water retention and Green and Ampt infiltration parameters, Transactions of the ASAE, 26(6), 1747-1752. https://doi.org/10.13031/2013.33837 Rawls, W. J., D. L. Brakensiek (1985), Prediction of soil water properties for hydrologic modeling, paper presented at Watershed management in the eighties, ASCE. Rawls, W. J., D. L. Brakensiek, K. Saxtonn (1982), Estimation of soil water properties, Transactions of the ASAE, 25(5), 1316-1320. https://doi.org/10.13031/2013.33720 Richards, L., L. Weaver (1943), Fifteen-atmosphere percentage as related to the permanent wilting percentage, Soil Science, 56(5), 331-340. https://doi.org/10.1097/00010694-194311000-00002 Rivers, E., R. Shipp (1978), Soil water retention as related to particle size in selected sands and loamy sands, Soil Science, 126(2), 94-100. https://doi.org/10.1097/00010694-197808000-00005 Ross, G. (1978), Relationships of specific surface area and clay content to shrink-swell potential of soils having different clay mineralogical compositions, Canadian Journal of Soil Science, 58(2), 159-166. https://doi.org/10.4141/cjss78-020 Rowell, D. (1994), Soil science: methods and applications, Department of Soil Science, University of Reading, Reading, UK. Salchow, E., R. Lal, N. Fausey, A. Ward (1996), Pedotransfer functions for variable alluvial soils in southern Ohio, Geoderma, 73(3-4), 165-181. https://doi.org/10.1016/0016-7061(96)00044-4 Saxton, K., W. J. Rawls, J. S. Romberger, R. Papendick (1986), Estimating generalized soil‐water characteristics from texture, Soil Science Society of America Journal, 50(4), 1031-1036. https://doi.org/10.2136/sssaj1986.03615995005000040054x Saxton, K. E., W. J. Rawls (2006), Soil water characteristic estimates by texture and organic matter for hydrologic solutions, Soil Science Society of America Journal, 70(5), 1569-1578. https://doi.org/10.2136/sssaj2005.0117 Schaap, M. G., W. Bouten (1996), Modeling water retention curves of sandy soils using neural networks, Water resources research, 32(10), 3033-3040. https://doi.org/10.1029/96WR02278 Schaap, M. G., F. J. Leij (1998), Database-related accuracy and uncertainty of pedotransfer functions, Soil Science, 163(10), 765-779. https://doi.org/10.1097/00010694-199810000-00001 Schaap, M. G., F. J. Leij, M. T. Van Genuchten (2001), Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, Journal of hydrology, 251(3-4), 163-176. https://doi.org/10.1016/S0022-1694(01)00466-8 Schaap, M. G., A. Nemes, M. T. Van Genuchten (2004), Comparison of models for indirect estimation of water retention and available water in surface soils, Vadose Zone Journal, 3(4), 1455-1463. https://doi.org/10.2113/3.4.1455 Schelle, H., L. Heise, K. Jänicke, W. Durner (2013), Water retention characteristics of soils over the whole moisture range: A comparison of laboratory methods, European Journal of Soil Science, 64(6), 814-821. https://doi.org/10.1111/ejss.12108 Sharma, S. K., B. P. Mohanty, J. Zhu (2006), Including topography and vegetation attributes for developing pedotransfer functions, Soil Science Society of America Journal, 70(5), 1430-1440. https://doi.org/10.2136/sssaj2005.0087 Shirazi, M. A., L. Boersma (1984), A unifying quantitative analysis of soil texture, Soil Science Society of America Journal, 48(1), 142-147. https://doi.org/10.2136/sssaj1984.03615995004800010026x Sojka, R., G. Lehrsch, S. Kostka, J. Reed, A. Koehn, J. Foerster (2009), Soil water measurements relevant to agronomic and environmental functions of chemically treated soil, Journal of the ASTM International, 6(1), 1-20. https://doi.org/10.1520/JAI101497 Stumpp, C., S. Engelhardt, M. Hofmann, B. Huwe (2009), Evaluation of pedotransfer functions for estimating soil hydraulic properties of prevalent soils in a catchment of the Bavarian Alps, European journal of forest research, 128, 609-620. https://doi.org/10.1007/s10342-008-0241-7 Troeh, F. R., L. M. Thompson (2005), Soils and soil fertility, Blackwell Iowa. Van Alphen, B., H. Booltink, J. Bouma (2001), Combining pedotransfer functions with physical measurements to improve the estimation of soil hydraulic properties, Geoderma, 103(1-2), 133-147. https://doi.org/10.1016/S0016-7061(01)00073-8 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. https://doi.org/10.2136/sssaj1980.03615995004400050002x Vereecken, H., J. Diels, J. Van Orshoven, J. Feyen, J. Bouma (1992), Functional evaluation of pedotransfer functions for the estimation of soil hydraulic properties, Soil Science Society of America Journal, 56(5), 1371-1378. https://doi.org/10.2136/sssaj1992.03615995005600050007x Wösten, J., Y. A. Pachepsky, W. Rawls (2001), Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics, Journal of hydrology, 251(3-4), 123-150. https://doi.org/10.1016/S0022-1694(01)00464-4 Wösten, J., A. Lilly, A. Nemes, C. Le Bas (1999), Development and use of a database of hydraulic properties of European soils, Geoderma, 90(3-4), 169-185. https://doi.org/10.1016/S0016-7061(98)00132-3 Weynants, M., H. Vereecken, M. Javaux (2009), Revisiting Vereecken pedotransfer functions: Introducing a closed-form hydraulic model, Vadose Zone Journal, 8(1), 86-95. https://doi.org/10.2136/vzj2008.0062 Yang, F., G.-L. Zhang, J.-L. Yang, D.-C. Li, Y.-G. Zhao, F. Liu, R.-M. Yang, F. Yang (2014), Organic matter controls of soil water retention in an alpine grassland and its significance for hydrological processes, Journal of hydrology, 519, 3086-3093. https://doi.org/10.1016/j.jhydrol.2014.10.054 Zacharias, S., G. Wessolek (2007), Excluding organic matter content from pedotransfer predictors of soil water retention, Soil Science Society of America Journal, 71(1), 43-50. https://doi.org/10.2136/sssaj2006.0098 Zhang, Y., M. G. Schaap (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. https://doi.org/10.1016/j.jhydrol.2017.01.004 吳晟哲 (2010), 土壤水力性質轉換函數之研究─以石門水庫集水區為例, 國立中興大學水土保持學研究所. https://doi.org/10.6845/NCHU.2010.01263 林可薇 (2012), 以連續土壤轉換函數預測 van Genuchten 模式參數之研究, 國立中興大學水土保持學研究所. https://doi.org/10.6845/NCHU.2012.00282 林俐玲, 陳威竹, 林可薇, 曹舜評 (2013), 以群集分析加強 van Genuchten 模式參數推估之研究, 中華水土保持學報, 44(4), 324-334. https://doi.org/10.29417/JCSWC.201312_44(4).0006 洪靖惠 (2008), 土壤水分特性曲線參數與物理性質關係之研究, 國立中興大學水土保持學研究所. https://doi.org/10.6845/NCHU.2008.00207 劉滄棽, 彭宗仁, 范家華, 郭鴻裕 (2007), 應用土壤轉換方程式 (PTF) 評估台灣平地土壤之飽和水力傳導度, 西太平洋地質科學. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90503 | - |
dc.description.abstract | 本研究以台灣中部農業土壤為範例,以土壤質地(砂、坋、黏含量)、總體密度與有機質含量作為解釋變數(或稱輸入特徵),應用多變量線性迴歸、隨機森林、類神經網路三種不同架構建立台灣本土土壤保水曲線的轉換方程式(pedotransfer function,PTF),並進一步分析PTF的輸入特徵重要性與不確定性。為了釐清不同輸入特徵對於PTF最終預測結果的影響力,本研究透過排列重要性的分析,得知PTF在基質勢能(matric potential)大於-0.1bar時,以總體密度作為最重要的輸入特徵;隨著基質勢能的減少總體密度重要性下降,坋粒與有機質含量重要性則逐漸上升。而為了評估非線性PTF的預測結果(不為常態分佈且具有較高偏度)之不確定性,本研究使用無母數自助法評估PTF的誤差95%信賴區間與95%預測區間。藉由衡量預測區間之實際涵蓋率,確認土壤含水量的實際涵蓋率皆介於95±1%,再次檢核無母數自助法可以有效評估非線性模型的不確定性。預測區間的建立除了提供PTF預測值的可靠度資訊外,此預測區間未來可用於檢驗土壤量測資料有效性的依據。最後,本研究成果也顯示三種不同架構的本土PTF,針對台灣土壤進行轉換的結果皆優於美國農業部開發之PTF─Rosetta3,再次確認建立台灣本土PTF的必要性。 | zh_TW |
dc.description.abstract | This study takes agricultural soils in central Taiwan as an example and uses soil texture (sand, silt, clay content), bulk density, and organic matter content as explanatory variables (or input features) to develop pedotransfer function (PTF) for the soil water retention curve using three different frameworks: multiple linear regression, random forest, and artificial neural networks. The study further analyzes the uncertainty and feature importance of PTF. Through permutation importance analysis, the study reveals that bulk density is the most important feature when the matric potential is larger than -0.1 bar in PTF predictions. As the matric potential decreases, the importance of bulk density decreases while the importance of silt and organic matter content gradually increases. To evaluate the uncertainty of the nonlinear PTF predictions, which do not follow a normal distribution and have higher skewness, the study uses the nonparametric bootstrap method to assess the 95% confidence intervals of PTF’s error and 95% prediction intervals of PTF. By measuring the coverage probability of the prediction intervals, the study confirms that the coverage probability of soil water content is approximately 95±1%, validating the effectiveness of the nonparametric bootstrap method in assessing the uncertainty of nonlinear models. In addition to providing reliability information for PTF predictions, the establishment of prediction intervals can also be used as a basis for testing the validity of soil measurement data. Finally, the results of this study demonstrate that the three different frameworks of local PTF outperform the PTF developed by United States Department of Agriculture(USDA), Rosetta3, in converting Taiwanese soils, reaffirming the necessity of developing local PTF for Taiwan. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T16:22:51Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-10-03T16:22:51Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審定書 #
謝誌 i 中文摘要 ii ABSTRACT iii 目錄 v 圖目錄 vii 表目錄 x 第1章 緒論 1 1.1 研究背景及動機 1 1.2 研究目的 2 第2章 文獻回顧 3 2.1 土壤保水曲線 3 2.1.1 土壤水總勢能 3 2.1.2 與土壤含水量之關係 3 2.1.3 VG model 5 2.2 土壤轉換方程式(Pedotransfer Function,PTF) 9 2.2.1 點型與連續型PTF 9 2.2.2 建立PTF的工具或方法 11 2.2.3 作為預測因子的土壤性質 14 2.2.4 轉換表現評估指標 17 2.2.5 不確定性分析 19 2.2.6 Rosetta 21 第3章 研究方法 23 3.1 研究架構及流程 23 3.1.1 土壤資料 24 3.1.2 土壤數據整理 28 3.1.3 Vw15bar補值 31 3.2 模型選擇與建置 33 3.2.1 多變量線性迴歸(Multiple Linear Regression,MLR) 33 3.2.2 隨機森林(Random Forest,RF) 34 3.2.3 類神經網路(Artificial Neural Network,ANN) 36 3.3 特徵重要性 38 3.3.1 基尼重要性(Gini importance) 38 3.3.2 排列重要性 40 3.4 不確定性分析 42 3.4.1 誤差期望值 42 3.4.2 預測區間 44 第4章 結果與討論 47 4.1 訓練、測試資料集探索與描述 47 4.2 模型訓練 56 4.2.1 多變量線性迴歸 56 4.2.2 隨機森林 59 4.2.3 類神經網路 60 4.2.4 土壤含水量訓練集預測結果 61 4.3 模型預測 62 4.3.1 土壤含水量測試集預測結果 62 4.3.2 參數重要性 62 4.3.3 誤差期望值之信賴區間 66 4.3.4 模型預測區間 67 4.4 與Rosetta3之比較 70 4.5 增加額外解釋變數的影響 73 4.6 點型與連續型PTF之不確定性比較 76 第5章 結論與建議 79 5.1 結論 79 5.2 建議 80 參考文獻 81 附錄 92 | - |
dc.language.iso | zh_TW | - |
dc.title | 應用機器學習建立台灣本土土壤保水曲線轉換方程式:以中部農業土壤為例 | zh_TW |
dc.title | Establish machine learning-based localized pedotransfer functions of soil water retention curves:a case study of agricultural soils in central Taiwan. | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 許健輝;廖國偉;胡明哲 | zh_TW |
dc.contributor.oralexamcommittee | Chien-Hui Syu;Kuo-Wei Liao;Ming-Che Hu | en |
dc.subject.keyword | 土壤轉換方程式,土壤保水曲線,排列重要性,不確定性分析,無母數自助法, | zh_TW |
dc.subject.keyword | pedotransfer function,soil water retention curve,permutation importance,uncertainty analysis,nonparametric bootstrap, | en |
dc.relation.page | 104 | - |
dc.identifier.doi | 10.6342/NTU202301530 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-07-13 | - |
dc.contributor.author-college | 生物資源暨農學院 | - |
dc.contributor.author-dept | 生物環境系統工程學系 | - |
顯示於系所單位: | 生物環境系統工程學系 |
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