Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world

Environmental Modelling and Software

Volumn 59, Issue , 2014, Pages 202-221

  Tayyebi, Amin ^a,b   Pijanowski, Bryan C ^b   Linderman, Marc ^c   Gratton, Claudio ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b Purdue University ^*  (United States)

^c UNIVERSITY OF IOWA   (United States)

Author keywords

Binary land use change simulation; Data mining; Global parametric model; Local non parametric model; Spatial and temporal comparison

Indexed keywords

AGRICULTURAL ROBOTS; FORESTRY; LAND USE; LOCATION; NEURAL NETWORKS; RIVERS; WATERSHEDS;

AGRICULTURAL EXPANSION; CLASSIFICATION AND REGRESSION TREE; LAND USE CHANGE MODELING; LAND-USE CHANGE; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; NON-PARAMETRIC MODEL; PARAMETRIC MODELING; SPATIAL AND TEMPORAL COMPARISON;

DATA MINING;

ARTIFICIAL NEURAL NETWORK; GLOBAL PERSPECTIVE; LAND USE CHANGE; SIMULATION; SPATIOTEMPORAL ANALYSIS; URBANIZATION;

FORESTRY; LAND USE; MODELS; NEURAL NETWORKS; RIVERS; WATER SHEDS;

EAST AFRICA; MICHIGAN; MUSKEGON BASIN; UNITED STATES; WISCONSIN;

EID: 84904599862     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2014.05.022     Document Type: Article

References (119)

1. Abdel-Aty M., Haleem K. Analyzing angle crashes at unsignalized intersections using machine learning techniques. Accid. Anal. Prev. 2011, 43(1):461-470.
2. Aertsen W., Kint V., Van Orshoven J., Muys B. Evaluation of modeling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA). Environ. Model. Softw. 2011, 26:929-937.
3. Alberti M., Marzluff J.M. Ecological resilience in urban ecosystems: linking urban patterns to human and ecological functions. Urban Ecosyst. 2004, 7(3):241-265.
4. Almeida A.P.G., Galão R.P., Sousa C.A., Novo M.T., Parreira R., Pinto J., Piedade J., Esteves A. Potential mosquito vectors of arboviruses in Portugal: species, distribution, abundance and West Nile infection. Trans. R. Soc. Trop. Med. Hyg. 2008, 102:823-832.
5. Areerachakul S., Sanguansintukul S. Classification and regression trees and MLP neural network to classify water quality of canals in Bangkok, Thailand. Int. J. Intell. Comput. Res. 2010, 1(1/2).
6. Armesto J.J., Smith-Ramírez C., Carmona M.R., Celis-Diez J.L., Díaz I.A., Gaxiola A., Gutiérrez A.G., Nuñez-Avila M.C., Pérez C.A., Rozzi R. Old-growth temperate rainforests of South America: conservation, plant-animal interactions, and baseline biogeochemical processes. Old-Growth Forests 2009, 367-390. Springer. C. Wirth, G. Gleixner, M. Heiman (Eds.).
7. Arnfield A.J. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol. 2003, 23(1):1-26.
8. Aspinall R.J. Modelling land use change with generalized linear and generalized additive models - a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana. J.Environ. Manag. 2004, 72:91-103.
9. Batty M., Xie Y. From cells to cities. Environ. Plan. B 1994, 21:s31-s48.
10. Bennett N.D., Croke B.F., Guariso G., Guillaume J.H., Hamilton S.H., Jakeman A.J., Andreassian V., et al. Characterizing performance of environmental models. Environ. Model. Softw. 2013, 40:1-20.
11. Bickel P.J., Doksum K.A. Mathematical Statistics. Basic Ideas and Selected Topics 2001, vol. I.
12. Bowen J.L., Valiela I. The ecological effects of urbanization of coastal watersheds: historical increases in nitrogen loads and eutrophication of Waquoit Bay estuaries. Can. J. Fish. Aquat. Sci. 2001, 58(8):1489-1500.
13. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth, Belmont, CA.
14. Brown D.E., Corruble V., Pittard C.L. Acomparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems. Pattern Recognit 1993, 26(6):953-961.
15. Büyükbebeci E.R.K.A. Comparison of MARS, CMARS and CART in Predicting Default Probabilities for Emerging Markets 2009, Term project at Institute of Applied Mathematics of METU, Ankara.
16. Clarke K.C., Hoppen S., Gaydos L. Aself-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environ. Plan. Plan. Des. 1997, 24:247-261.
17. Czech B., Krausman P.R., Devers P.K. Economic associations among causes of species endangerment in the United States associations among causes of species endangerment in the United States reflect the integration of economic sectors, supporting the theory and evidence that economic growth proceeds at the competitive exclusion of nonhuman species in the aggregate. BioScience 2000, 50(7):593-601.
18. De Andrés Suárez J., Lorca Fernández P., Cos Juez F.J.de, Sánchez-Lasheras F. Bankruptcy forecasting: a hybrid approach using Fuzzy c-means clustering and multivariate adaptive regression splines. Expert Syst. Appl. 2011, 38(3):1866-1875.
19. Diaz G.I., Nahuelhual L., Echeverría C., Marín S. Drivers of land abandonment in Southern Chile and implications for landscape planning. Lands. Urban Plan. 2011, 99:207-217.
20. Ewert F., Rounsevell M.D.A., Reginster I., Metzger M.J., Leemans R. Future scenarios of European agricultural land use: I. Estimating changes in crop productivity. Agric. Ecosyst. Environ. 2005, 107(2):101-116.
21. FAO Seed Security for Food Security in the Light of Climate Change and Soaring Food Prices: Challenges and Opportunities 2009, FAO, Rome. ftp://ftp.fao.org/docrep/fao/meeting/016/k4275e.pdf.
22. Fielding A.H., Bell J.F. Areview of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 1997, 24:38-49.
23. Foley J.A., DeFries R., Asner G.P., Barford C., Bonan G., Carpenter S.R., Snyder P.K. Global consequences of land use. Science 2005, 309(5734):570-574.
24. Francis L. Neural Networks Demystified 2001, 253-320. Casualty Actuarial Society Forum.
25. Friedman J.H., Silverman B.W. Flexible parsimonious smoothing and additive modeling. Technometrics 1989, 31:3-39.
26. Friedman J.H. Multivariate adaptive regression splines. Ann. Stat. 1991, 19:1.
27. Gansky S.A. Dental data mining: potential pitfalls and practical issues. Adv. Dent. Res. 2003, 17:109-114.
28. Gelfand S.B., Delp E.J. On tree structured classifiers 1991, 51-70. Sethi, Jain (Eds.).
29. Grimm N.B., Faeth S.H., Golubiewski N.E., Redman C.L., Wu J., Bai X., Briggs J.M. Global change and the ecology of cities. Science 2008, 319(5864):756-760.
30. Grimmond C.S.B., King T.S., Cropley F.D., Nowak D.J., Souch C. Local-scale fluxes of carbon dioxide in urban environments: methodological challenges and results from Chicago. Environ. Pollut. 2002, 116:S243-S254.
31. Haqen D.A., Vincent J.W., Welle P.G. Benefits of preserving old-growth forests and the spotted owl. Contemp. Econ. Policy 1992, 10(2):13-26.
32. Hardle W., Müller M., Sperlich S., Werwatz A. Nonparametric and Semiparametric Models 2004, Springer Series in Statistics, Berlin.
33. He Z., Lo C. Modeling urban growth in Atlanta using logistic regression. Comput. Environ. Urban Syst. 2007, 31(6):667-688.
34. Herold M.M. Ajoint initiative for harmonization and validation of land cover datasets. IEEE Trans. Geosci. Remote Sens. 2006, 44:1719-1727.
35. Imran K., Ture M.A., Kurum T. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst. Appl. 2008, 34:366-374.
36. Jokar A.J., Helbich M., Vaz E. Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran. Cities 2013, 32:33-42.
37. Jokar A.J., Helbich M., Kainz W., Darvishi B.A. Integration of logistic regression and Markov chain models to simulate urban expansion. Int. J. Appl. Earth Observ. Geoinf. 2013, 21:265-275.
38. Jokar A.J., Helbich M., Mousivand A.J. Amorphological approach to predicting urban expansion. Trans. GIS 2014, 18(2):219-233.
39. Jenerette G.D., Wu J. Analysis and simulation of land-use change in the central Arizona-Phoenix region, USA. Landsc. Ecol. 2001, 16(7):611-626.
40. Kayri M. The analysis of internet addiction scale using multivariate adaptive regression splines. Iran. J. Publ. Health 2010, 39(4):51-63.
41. Kuhnert P.M., Do K.A., McClure R. Combining non-parametric models with logistic regression: an application to motor vehicle injury data. Comput. Stat. Data Anal. 2000, 34(3):371-386.
42. Lambin E.F., Meyfroidt P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl. Acad. Sci. U.S.A. 2011, 108(9):3465-3472.
43. Landis J., Zhang M. The second generation of the California urban futures model: part 2. Specification and calibration results of the land-use change sub-model. Environ. Plan. A 1998, 25:795-824.
44. Lawrence S., Giles C.L., Tsoi A.C. Lessons in neural network training: overfitting may be harder than expected. Proceedings of the Fourteenth National Conference on Artificial Intelligence 1997, 540-545.
45. LeBlanc M. An Adaptive Expansion Method for Regression 1993, Technical report, Department of Statistics, University of Toronto.
46. Letourneau A., Verburg P.H., Stehfest E. Aland-use systems approach to represent land-use dynamics at continental and global scales. Environ. Model. Softw. 2012, 33:61-79.
47. Lewis R.J. An introduction to classification and regression tree (CART) analysis. Annual Meeting of the Society for Academic Emergency Medicine in San Francisco, California 2000, 1-14.
48. Li X., Nsofor G.C., Song L. Acomparative analysis of predictive data mining techniques. Int. J. Rapid Manuf. 2009, 1(2):150-172.
49. Liu C., Shum H.Y., Zhang C.S. Atwo-step approach to hallucinating faces: global parametric model and local nonparametric model. Computer Vision and Pattern Recognition. Proceedings of the IEEE Computer Society Conference on 2001, vol. 1:1-192.
50. Manel S., Dias J.M., Ormerod S.J. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecol. Model. 1999, 120:337-347.
51. Mas J.F., Velázquez A., Díaz-Gallegos J.R., Mayorga-Saucedo R., Alcantara C., Bocco G., Castro R., Fernández T., Pérez-Vega A. Assessing land/use cover changes: a nationwide multidate spatial database for Mexico. Int. J. Appl. Earth Observ. Geoinf. 2004, 5:249-261.
52. Matthews R., Gilbert N., Roach A., Polhill J., Gotts N. Agent-based land-use models: a review of applications. Landsc. Ecol. 2007, 22:1447-1459.
53. Mertens B., Lambin E.F. Land-cover-change trajectories in Southern Cameroon. Ann. Assoc. Am. Geogr. 2000, 90(3):467-494.
54. Meehan T.D., Gratton C., Diehl E., Hunt N.D., Mooney D.F., Ventura S.J., Jackson R.D., et al. Ecosystem-service tradeoffs associated with switching from annual to perennial energy crops in riparian zones of the US Midwest. PLoS One 2013, 8(11):e80093.
55. Moisen G.G., Frescino T.S. Comparing five modeling techniques for predicting forest characteristics. Ecol. Model. 2002, 157:209-225.
56. Muñoz J., Felicísimo A.M. Comparison of statistical methods commonly used in predictive modeling. J.Veget. Sci. 2004, 15:285-292.
57. Müller D., Leitão P.J., Sikor T. Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees. Agric. Syst. 2013, 117:66-77.
58. Nair P.K.R. An Introduction to Agroforestry 1993, Kluwer Academic Publishers, Dordrecht, Netherlands.
59. NRC Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties 2005, National Research Council, 208 pp.
60. NCR Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond 2007, National Research Council, 456 pp.
61. Olson J., Alagarswamy G., Andresen J., Campbell D., Davis A., Ge J., Huebner M., Lofgren B., Lusch D., Moore N., Pijanowski B., Qi J., Thornton P., Torbick N., Wang J. Integrating diverse methods to understand climate-land interactions in East Africa. GeoForum 2008, 39(2):898-911.
62. Peng Y., Lohmann U., Leaitch R., Banic C., Couture M. The cloud albedo-cloud droplet effective radius relationship for clean and polluted clouds from RACE and FIRE. ACE. J.Geophys. Res.: Atmos. (1984-2012) 2002, 107(D11):AAC-1.
63. Pielke R.A. Land use and climate change. Science 2005, 310(5754):1625-1626.
64. Pijanowski B.C., Brown Daniel G., Shellito Bradley A., Manik Gaurav A. Using neural networks and GIS to forecast land use changes: a land transformation model. Comput. Environ. Urban Syst. 2002, 26:553-575.
65. Pijanowski B.C., Pithadia S., Shellito B.A., Alexandridis K. Calibrating a neural network based urban change model for two metropolitan areas of Upper Midwest of the United States. Int. J. Geogr. Inf. Sci. 2005, 19:197-215.
66. Pijanowski B.C., Alexandridis K., Mueller D. Modeling urbanization patterns in two diverse regions of the world. J.Land Use Sci. 2006, 1:83-108.
67. Pijanowski B.C., Tayyebi A., Delavar M.R., Yazdanpanah M.J. Urban expansion simulation using geographic information systems and artificial neural networks. Int. J. Environ. Res. 2009, 3(4):493-502.
68. Pijanowski B.C., Moore N., Mauree D., Niyogi D. Evaluating error propagation in coupled land-atmosphere models. Earth Interact. 2011, 15:1-25.
69. Pijanowski B.C., Robinson K.D. Rates and patterns of land use change in the Upper Great Lakes States, USA: a framework for spatial temporal analysis. Landsc. Urban Plan. 2011, 102(2):102-116.
70. Pijanowski B.C., Tayyebi A., Doucette J., Pekin B.K., Braun D., Plourde J. Abig data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing environment. Environ. Model. Softw. 2014, 51:250-268.
71. Pontius R.G., Schneider L. Land-use change model validation by a ROC method for the Ipswich watershed, Massachusetts, USA. Agric. Ecosyst. Environ. 2001, 85(1-3):239-248.
72. Pontius R.G., Batchu K. Using the relative operating characteristic to quantify certainty in prediction of location of land cover change in India. Trans. GIS 2003, 7(4):467-484.
73. Pontius R.G., Boersma W., Castella J.C., Clarke K., de Nijs T., Dietzel C., Duan Z., Fotsing E., Goldstein N., Kok K., Koomen E., Lippitt C.D., McConnell W., Mohd Sood A., Pijanowski B.C., Pithadia S., Sweeney S., Trung T.N., Veldkamp A.T., Verburg P.H. Comparing input, output, and validation maps for several models of land change. Ann. Reg. Sci. 2008, 42(1):11-47.
74. Psichogios D.C., Ungar L.H. Ahybrid neural network-first principles approach to process modeling. AIChE J. 1992, 38:1499-1511.
75. Quirós E., Felicísimo Á.M., Cuartero A. Testing multivariate adaptive regression splines (MARS) as a method of land cover classification of TERRA-ASTER satellite images. Sensors 2009, 9(11):9011-9028.
76. Ralha C.G., Abreu C.G., Coelho C.G., Zaghetto A., Macchiavello B., Machado R.B. Amulti-agent model system for land-use change simulation. Environ. Model. Softw. 2013, 42:30-46.
77. Ray D.K., Pijanowski B.C. Abackcast land use change model to generate past land use maps: application and validation at the Muskegon river watershed of Michigan, USA. J.Land Use Sci. 2010, 5(1):1-29.
78. Ray D.K., Pijanowski B.C., Kendall A.D., Hyndman D.W. Coupling land use and groundwater models to map land use legacies: assessment of model uncertainties relevant to land use planning. Appl. Geogr. 2012, 34:356-370.
79. Refaeilzadeh P., Tang L., Liu H. Cross-validation http://www.public.asu.edu/%7Eltang9/papers/ency-cross-validation.pdf.
80. Rhemtulla J.M., Mladenoff D.J., Clayton M.K. Regional land-cover conversion in the US upper Midwest: magnitude of change and limited recovery (1850-1935-1993). Landsc. Ecol. 2007, 22(1):57-75.
81. Sala O.E., Chapin F.S., Armesto J.J., Berlow E., Bloomfield J., Dirzo R., Wall D.H., et al. Global biodiversity scenarios for the year 2100. Science 2000, 287(5459):1770-1774.
82. Seppelt R., Voinov A. Optimization methodology for land use patterns using spatially explicit landscape models. Ecol. Model. 2002, 151(2/3):125-142.
83. Seppelt R., Richter O. It was an artefact not the result: a note on systems dynamic model development tools. Environ. Model. Softw. 2005, 20(12):1543-1548.
84. Serneels S., Lambin E.F. Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model. Agric. Ecosyst. Environ. 2001, 85:65-81.
85. Soares-Filho B., Rodrigues H., Follador M. Ahybrid analytical-heuristic method for calibrating land-use change models. Environ. Model. Softw. 2013, 43:80-87.
86. Steinberg D., Colla P. CART-Classification and Regression Trees: a Supplementary Manual for Windows 1997, Salford Systems Inc., San Diego.
87. Steinberg D., Golovnya M. CART 6.0 User's Manual 2006, Salford Systems, San Diego, CA.
88. Stevens D., Dragićević S. AGIS-based irregular cellular automata model of land-use change. Environ. Plan.: Plan. Des. 2007, 34:708-724.
89. Sut N., Simsek O. Comparison of regression tree data mining methods for prediction of mortality in head injury. Expert Syst. Appl. 2011, 38:15534-15539.
90. Swenson J.J., Franklin J. The effects of future urban development on habitat fragmentation in the Santa Monica Mountains. Landsc. Ecol. 2000, 15(8):713-730.
91. Swets J.A., Pickett R.M. Evaluation of Diagnostic Systems: Methods from Signal Detection Theory 1982, Academic Press, New York.
92. Tang Z., Engel B., Lim K., Pijanowski B., Harbor J. Minimizing the impact of urbanization on long-term runoff. J.Water Resour. Assoc. 2005, 41(6):1347-1359.
93. Tang Z., Engel B.A., Pijanowski B.C., Lim K.J. Forecasting land use change and its environmental impact at a watershed scale. J.Environ. Manag. 2005, 76:35-45.
94. Tayyebi A., Delavar M.R., Saeedi S., Amini J. Monitoring the urban expansion by multi-temporal GIS maps. Application of Remote Sensing and Imagery (TS 5B), Integrating Generations, FIG Working Week 2008 and FIG/UNHABITAT Seminar 2008.
95. Tayyebi A., Delavar M.R., Saeedi S., Amini J., Alinia H. Monitoring land use change by multi-temporal Landsat remote sensing imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, XXXVII.
96. Tayyebi A., Delavar M.R., Pijanowski B.C., Yazdanpanah M.J. Accuracy assessment in urban expansion model. Spatial Data Quality, from Process to Decisions 2009, 107-115. CRC Press, Canada. R. Devillers, H. Goodchild, Taylor, Francis (Eds.).
97. Tayyebi A., Delavar M.R., Yazdanpanah M.J., Pijanowski B.C., Saeedi S., Tayyebi A.H. Aspatial logistic regression model for simulating land use patterns: a case study of the Shiraz Metropolitan area of Iran. Advances in Earth Observation of Global Change 2010, 27-42. Springer, Netherlands.
98. Tayyebi A.H., Delavar M.R., Tayyebi A., Golobi M. Combining multi criteria decision making and Dempster Shafer theory for landfill site selection. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2010, 38(8):1073-1078.
99. Tayyebi A., Pijanowski B.C., Tayyebi A.H. An urban growth boundary model using neural networks, GIS and radial parameterization: an application to Tehran, Iran. Landsc. Urban Plan. 2011, 100:35-44.
100. Tayyebi A., Pijanowski B.C., Pekin B. Two rule-based urban growth boundary models applied to the Tehran metropolitan area, Iran. Appl. Geogr. 2011, 31:908-918.
101. Tayyebi A. Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms 2013, Order no. 3606166 Purdue University, ProQuest, Ann Arbor, Web. 15 May 2014.
102. Tayyebi A., Pekin B.K., Pijanowski B.C., Plourde J.D., Doucette J.S., Braun D. Hierarchical modeling of urban growth across the conterminous USA: developing meso-scale quantity drivers for the land transformation model. J.Land Use Sci. 2013, 8(4):422-442.
103. Tayyebi A., Perry P.C., Tayyebi A.H. Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster-vector routines with remote sensing and GIS. Int. J. Geogr. Inf. Sci. 2013, 1-21. (ahead-of-print).
104. Tayyebi A.H., Tayyebi A., Khanna N. Assessing uncertainty dimensions in land-use change models: using swap and multiplicative error models for injecting attribute and positional errors in spatial data. Int. J. Remote Sens. 2014, 35(1):149-170.
105. Tayyebi A., Pijanowski B.C. Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. Int. J. Appl. Earth Observ. Geoinf. 2014, 28:102-116.
106. Theobald D.M., Hobbs N.T. Forecasting rural land use change: a comparison of regression- and spatial transition-based models. Geogr. Environ. Model. 1998, 2(1):57-74.
107. Timofeev R. Classification and Regression Trees (CART) Theory and Applications 2004, (Master's thesis), Humboldt University Berlin.
108. Ture M., Kurt I., Kurum A.T., Ozdamar K. Comparing classification techniques for predicting essential hypertension. Expert Syst. Appl. 2005, 29:583-588.
109. Turner B.L., Skole D., Sanderson S., Fischer G., Fresco L., Leemans R. Land-Use and Land-Cover Change Science/Research Plan 1995, IGBP report no. 35 and HDP report no. 7.
110. Vayssières M.P., Plant R.E., Allen-Diaz B.H. Classification trees: an alternative non-parametric approach for predicting species distributions. J.Veget. Sci. 2000, 11(5):679-694.
111. Verburg P.H., Van De Steeg J., Veldkamp A., Willemen L. From land cover change to land function dynamics: a major challenge to improve land characterization. J.Environ. Manag. 2009, 90(3):1327-1335.
112. Warner B., Misra M. Understanding neural networks as statistical tools. Am. Stat. 1996, 50(4):284-293.
113. Washington-Ottombre C., Pijanowski B., Campbell D., Olson J., Maitima J., Musili A., Mwangi A. Using a role-playing game to inform the development of land-use models for the study of a complex socio-ecological system. Agric. Syst. 2010, 103(3):117-126.
114. Weinhold D. Estimating the loss of agricultural productivity in the Amazon. Ecol. Econ. 1999, 31(1):63-76.
115. Wernick I.K. Global Warming and the Industrial System 2007, International Relations and Security Network (ISN), Zurich, Switzerland, See. http://www.isn.ethz.ch/pubs/ph/details.cfm?lng=en%26id=30366.
116. Willert M.V., Windhorst K., Techel G. Modeling of Forest Growth and Yield in Uganda 2010, Final report, Study Commissioned by SPGS, Kampala.
117. Zha W., Chan W.-Y. Objective speech quality measurement using statistical data mining. EURASIP J. Appl. Signal Process. 2005, 9:1410-1424.
118. Zhao Z. Parametric and nonparametric models and methods in financial econometrics. Stat. Surv. 2008, 2:1-42. 1935-7516.
119. Zurada J.M. Introduction to Artificial Neural Systems 1992, PWS Publishing Company, Boston.