ارزیابی تناسب ارضی با استفاده از رویکردهای سنتی و مدلهای یادگیری ماشینی (مطالعه موردی: دشت آبیک، استان قزوین)

AbdelRahman, M. A. E., Saleh, A. M., & Arafat, S. M. (2022). Assessment of land suitability using a soil-indicator-based approach in a geomatics environment. Scientific Reports, 12(1), 18113.

Akıncı, H., Özalp, A. Y., & Turgut, B. (2013). Agricultural land use suitability analysis using GIS and AHP technique. Computers and Electronics in Agriculture, 97, 71–82.

Alexandratos, N., & Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision.

Al-Mashreki, M. H., Juhari, M. A., Sahibin, A. R., Desa, K. M., Tukimat, L., & Haider, A. R. (2011). Land suitability evaluation for sorghum crop in the Ibb Governorate, Republic of Yemen using remote sensing and GIS techniques. Australian Journal of Basic and Applied Sciences, 5(3), 359–368.

Ashraf, S., & Normohammadan, B. (2011). Qualitative evaluation of land suitability for wheat in Northeast-Iran Using FAO methods. Indian Journal of Science and Technology, 4(6), 703–707.

Bagheri Bodaghabadi, M., Martínez‐Casasnovas, J. A., Khakili, P., Masihabadi, M. H., & Gandomkar, A. (2015). Assessment of the FAO traditional land evaluation methods, A case study: Iranian Land Classification method. Soil Use and Management, 31(3), 384–396.

Behrens, T., & Scholten, T. (2006). Chapter 25 A Comparison of Data-Mining Techniques in Predictive Soil Mapping. In P. Lagacherie, A. B. McBratney, & M. Voltz (Eds.), Developments in Soil Science (Vol. 31, pp. 353–617). Elsevier. https://doi.org/https://doi.org/10.1016/S0166-2481(06)31025-2

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., & Edwards Jr, T. C. (2015). Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239, 68–83.

Daigle, J. J., Hudnall, W. H., Gabriel, W. J., Mersiovsky, E., & Nielson, R. D. (2005). The National Soil Information System (NASIS): Designing soil interpretation classes for military land-use predictions. Journal of Terramechanics, 42(3–4), 305–320.

da Silva, A. F., Barbosa, A. P., Zimback, C. R. L., Landim, P. M. B., & Soares, A. (2015). Estimation of croplands using indicator kriging and fuzzy classification. Computers and Electronics in Agriculture, 111, 1–11. https://doi.org/https://doi.org/10.1016/j.compag.2014.11.020

Dorling, D. (2021). World population prospects at the UN: our numbers are not our problem? In The Struggle for Social Sustainability (pp. 129–154). Policy Press.

FAO, F. A. O. (n.d.). of the UN 1976. A framework for land evaluation. Soil Bulletin, 32, 72.

Food and Agriculture Organization of the United Nations. Soil Resources and Conservation Service, M. (1985). Guidelines, Land Evaluation for Irrigated Agriculture. FAO.

Gu, C., Mu, X., Gao, P., Zhao, G., Sun, W., Tatarko, J., & Tan, X. (2019). Influence of vegetation restoration on soil physical properties in the Loess Plateau, China. Journal of Soils and Sediments, 19(2), 716–728. https://doi.org/10.1007/s11368-018-2083-3

Gu, G., Wu, B., Zhang, W., Lu, R., Feng, X., Liao, W., Pang, C., & Lu, S. (2023). Comparing machine learning methods for predicting land development intensity. Plos One, 18(4), e0282476.

Hagos, Y. G., Mengie, M. A., Andualem, T. G., Yibeltal, M., Linh, N. T. T., Tenagashaw, D. Y., & Hewa, G. (2022). Land suitability assessment for surface irrigation development at Ethiopian highlands using geospatial technology. Applied Water Science, 12(5), 98.

IaW, F. A. O. (2015). Achieving Zero Hunger: The Critical Role of Investments in Social Protection and Agriculture, Agricultural Development Economics Division. Rome: FAO.

Igrejas, G., & Branlard, G. (2020). The importance of wheat. Wheat Quality for Improving Processing and Human Health, 1–7.

Islami, F. A., Tarigan, S. D., Wahjunie, E. D., & Dasanto, B. D. (2022). Accuracy assessment of land use change analysis using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conference Series: Earth and Environmental Science, 950(1), 012091.

Khaledian, Y., & Miller, B. A. (2020). Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling, 81, 401–418. https://doi.org/https://doi.org/10.1016/j.apm.2019.12.016

Khamoshi, S. E., Sarmadian, F., & Omid, M. (2023). Predicting and Mapping of Soil Organic Carbon Stock Using Machin Learning Algorithm, Iranian Journal of Soil and Water Research, 53 (11), 2671-2681. (In Persian)

Kidd, D., Webb, M., Malone, B., Minasny, B., & McBratney, A. (2015). Digital soil assessment of agricultural suitability, versatility and capital in Tasmania, Australia. Geoderma Regional, 6, 7–21.

Kılıc, O. M., Ersayın, K., Gunal, H., Khalofah, A., & Alsubeie, M. S. (2022). Combination of fuzzy-AHP and GIS techniques in land suitability assessment for wheat (Triticum aestivum) cultivation. Saudi Journal of Biological Sciences, 29(4), 2634–2644. https://doi.org/https://doi.org/10.1016/j.sjbs.2021.12.050

Lagacherie, P., Arrouays, D., Bourennane, H., Gomez, C., & Nkuba-Kasanda, L. (2020). Analysing the impact of soil spatial sampling on the performances of Digital Soil Mapping models and their evaluation: A numerical experiment on Quantile Random Forest using clay contents obtained from Vis-NIR-SWIR hyperspectral imagery. Geoderma, 375, 114503.

Liu, X., Wang, J., & Song, X. (2023). Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China. Remote Sensing, 15(7). https://doi.org/10.3390/rs15071847

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/https://doi.org/10.1016/j.geodrs.2020.e00260

McBratney, A. B., Santos, M. L. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.

Minasny, B., & McBratney, A. B. (2016). Digital soil mapping: A brief history and some lessons. Geoderma, 264, 301–311.

Mokarram, M., Hamzeh, S., Aminzadeh, F., & Zarei, A. R. (2015). Using machine learning for land suitability classification. West African Journal of Applied Ecology, 23(1), 63–73.

Mugiyo, H., Chimonyo, V. G. P., Sibanda, M., Kunz, R., Masemola, C. R., Modi, A. T., & Mabhaudhi, T. (2021). Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review. Land, 10(2). https://doi.org/10.3390/land10020125

Neyestani, M., Sarmadian, F., Jafari, A., Keshavarzi, A., & Sharififar, A. (2021). Digital mapping of soil classes using spatial extrapolation with imbalanced data. Geoderma Regional, 26, e00422. https://doi.org/https://doi.org/10.1016/j.geodrs.2021.e00422

Onyutha, C. (2019). African food insecurity in a changing climate: The roles of science and policy. Food and Energy Security, 8(1), e00160.

Prakash, T. N. (2003). Land suitability analysis for agricultural crops: a fuzzy multicriteria decision making approach.

Roell, Y. E., Beucher, A., Møller, P. G., Greve, M. B., & Greve, M. H. (2020). Comparing a Random Forest Based Prediction of Winter Wheat Yield to Historical Yield Potential. Agronomy, 10(3). https://doi.org/10.3390/agronomy10030395

Rossiter, D. G. (2000). Methodology for soil resource inventories. ITC Lecture Notes SOL, 27.

Safari, Y., Esfandiarpour-Boroujeni, I., Kamali, A., Salehi, M. H., & Bagheri-Bodaghabadi, M. (2013). Qualitative land suitability evaluation for main irrigated crops in the shahrekord plain, Iran: A geostatistical approach compared with conventional method. Pedosphere, 23(6), 767–778.

Stoorvogel, J. J., Kempen, B., Heuvelink, G. B. M., & De Bruin, S. (2009). Implementation and evaluation of existing knowledge for digital soil mapping in Senegal. Geoderma, 149(1–2), 161–170.

Sys, C., Van Ranst, E., & Debaveye, J. (1991). Land evaluation: principles in land evaluation and crop production calculations. General Administration for Development Cooperation.

Taghizadeh-Mehrjardi, R., Nabiollahi, K., Rasoli, L., Kerry, R., & Scholten, T. (2020). Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy, 10(4). https://doi.org/10.3390/agronomy10040573

Takoutsing, B., & Heuvelink, G. B. M. (2022). Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors. Geoderma, 428, 116192. https://doi.org/https://doi.org/10.1016/j.geoderma.2022.116192

Vasu, D., Srivastava, R., Patil, N. G., Tiwary, P., Chandran, P., & Singh, S. K. (2018). A comparative assessment of land suitability evaluation methods for agricultural land use planning at village level. Land Use Policy, 79, 146–163.

Wang, B., Waters, C., Orgill, S., Cowie, A., Clark, A., Li Liu, D., Simpson, M., McGowen, I., & Sides, T. (2018). Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecological Indicators, 88, 425–438. https://doi.org/10.1016/J.ECOLIND.2018.01.049

Wylie, B. K., Pastick, N. J., Picotte, J. J., & Deering, C. A. (2019). Geospatial data mining for digital raster mapping. GIScience & Remote Sensing, 56(3), 406–429.

Zakarya, Y. M., Metwaly, M. M., AbdelRahman, M. A. E., Metwalli, M. R., & Koubouris, G. (2021). Optimized land use through integrated land suitability and GIS approach in West El-Minia Governorate, Upper Egypt. Sustainability, 13(21), 12236.

Zhang, M., Shi, W., & Xu, Z. (2020). Systematic comparison of five machine-learning models in classification and interpolation of soil particle size fractions using different transformed  data. Hydrology and Earth System Sciences, 24(5), 2505–2526. https://doi.org/10.5194/hess-24-2505-2020

Ziadat, F. M. (2000). Application of GIS and remote sensing for land use planning in the arid areas of Jordan. Cranfield University (United Kingdom).