A GIS methodological framework based on fuzzy sets theory for land use management

Kurtener, Dmitry; Badenko, Vladimir

doi:10.1590/s0104-65002000000100004

Cited by 25 publications

(14 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the present time, methodologies for modelling with use of GIS are developed [21][22][23][24]. In Figure 5 a part of satellite image of river basin in cross-section of flood-control DWR and flood hazard area corresponding to water plane of flood passing with different probability, designed with use of digital elevation model in GIS environment, is shown.…”

Section: Resultsmentioning

confidence: 99%

Distributed System of Dams for Flood Protection of Urban Land to Ensure Sustainable Development

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. The article considers innovative materials and structures for multipurpose flood protection self-regulated dams. Russian and foreign experiences in flood consequence, as well as conceptions for protection of urban land in the context of climate change are analyzed. Method for flood protection is proposed and is proved by creation of distributed system of self-regulated flood-control distributed system of dams in a river basin.

show abstract

Section: Resultsmentioning

confidence: 99%

Distributed System of Dams for Flood Protection of Urban Land to Ensure Sustainable Development

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Examples where fuzzy indicators have been successfully applied include zoning of territory contaminated by heavy metals (Kurtener et al, 1999;Kurtener & Badenko, 2002), multi-dimensional assessment of urban areas after flooding (Kurtener et al, 1999), assessment of polluted agricultural fields to develop strategies for remediation (Kurtener et al, 1999), and assessment of burned forest areas for land restoration planning . Other successful applications include assessing land suitability for agricultural experimentation (Kurtener & Badenko, 2000b), assessing agricultural lands for site-specific residue management (Kurtener & Badenko, 2000c), and multidimensional evaluations of areas for land markets (Yakishev et al, 2000).…”

Section: Fuzzy Indicator Modelingmentioning

confidence: 99%

“…Recently GISFM approach has been developed successfully , 2000a, 2000b, 2000c, 2000d, 2001a, 2001b, 2013Kurtener and Krueger, 2006;Kurtener et al, 1999Kurtener et al, , 2000dKurtener et al, , 2001aKurtener et al, , 2001bKurtener et al, , 2001cKurtener et al, , 2002Kurtener et al, , 2002aKurtener et al, , 2003Kurtener et al, , 2004Kurtener et al, , 2004aKurtener et al, , 2004bRamli & Baja, 2005;. Yakishev et al, 2000).…”

Section: Combination Of Fuzzy Indicator Models With Geographical Infomentioning

confidence: 99%

Utilization of Fuzzy Computing in Agrophysics

Kurtener¹,

Yakushev²

2014

EAJ

Self Cite

View full text Add to dashboard Cite

Traditionally decision of agrophysics problems was evaluated with problem-oriented models. Utilization of fuzzy computing gives new dimension for agrophsics. It is well known that in agricultural sciences there exists much fuzzy knowledge, that is, knowledge which is vague, imprecise, uncertain, ambiguous, inexact, or probabilistic in nature. Fuzzy Computing is power tools for dealing with randomness and uncertainties. This paper reviews exploitation of fuzzy computing in agrophysics. The emphasis is on the achievements of Agrophysical Research Institute, St. Petersburg, Russia, especially in the fields of soil tillage, crop management, precision agriculture, melioration, and agricultural engineering.

show abstract

“…Recently several tools, based on fuzzy sets theory and fuzzy logic, have been developed for decision support regarding the problems of land evaluation (Burrough, 1986(Burrough, , 1989Burrough et al, 1992;Baja et al, 2002Baja et al, , 2007Krueger-Shvetsova & Kurtener, 2003;Kurtener & Badenko, 2000Ramli & Baja, 2007).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Agricultural Soil Resources Using Fuzzy Modeling

Kurtener¹,

Sukhanov²

2014

JAS

View full text Add to dashboard Cite

Assessment of agricultural soil resources is a very difficult because exists knowledge is much fuzzy. Developed fuzzy model is effective tool for dealing with randomness and uncertainties. This model is based on a two-level system of fuzzy indicators. The first level -is individual fuzzy indicators (IFI), reflecting the assessment of individual characteristics. Second level -is combined fuzzy indicators (CFI), reflecting a combination of individual indicators. IFI are developed for the four characteristics of the soil (humus, amount absorbed cations, acidity (pH) and physical clay content). The proposed method is illustrated with a simple example.

show abstract

A GIS methodological framework based on fuzzy sets theory for land use management

Cited by 25 publications

References 4 publications

Distributed System of Dams for Flood Protection of Urban Land to Ensure Sustainable Development

Distributed System of Dams for Flood Protection of Urban Land to Ensure Sustainable Development

Utilization of Fuzzy Computing in Agrophysics

Evaluation of Agricultural Soil Resources Using Fuzzy Modeling

Contact Info

Product

Resources

About