Soil pattern inference using GIS under fuzzy logic. by A-Xing Zhu Download PDF EPUB FB2
A geographical information system (GIS) or expert knowledge‐based fuzzy soil inference scheme (soil‐land inference model, SoLIM) is described. The scheme consists of three major components: (i) a model employing a similarity representation of soils, (ii) a set of inference techniques for deriving the similarity representation, and (iii) use.
A geographical information system (GIS) or expert knowledge‐based fuzzy soil inference scheme (soil‐land inference model, SoLIM) is described. The scheme consists of three major components: (i) a model employing a similarity representation of soils, (ii) a set of inference techniques for deriving the similarity representation, and (iii) use Cited by: ZHU ET AL.: SOIL MAPPING USING GIS, EXPERT KNOWLEDGE, AND FUZZY LOGIC tion of soils in the spatial domain and (ii)the similarity representation of soils in the parameter domain.
Under raster GIS data modeling, an area can be represented by many small squares (pixels). The pixel size can be very small; it is often 30 m on each side. 1 SoLIM: A New Technology For Soil Mapping Using GIS, Expert Knowledge & Fuzzy Logic Overview Prepared by A-Xing Zhu1, James E. Burt1, Amanda C.
Moore2, Michael P. Smith1, Jian Liu1, Feng Qi1 1Department of Geography University of Wisconsin-Madison. The automated soil inference under fuzzy logic is based on the concept that soil (S) is a function (f) of its formative environment (E).
Soil inference process. The knowledgebase contains. PDF | On Jan 1,A-Xing Zhu and others published SoLIM: A New Technology For Soil Mapping Using GIS, Expert Knowledge & Fuzzy Logic Overview. This study developed a method for Soil Organic Matter (SOM) mapping in a lithoidal mountainous area using a stratified strategy under fuzzy inference framework.
Environmental variables derived from terrain analysis and remote sensing incorporated with a small number of field samples were used to predict spatial variation of SOM. A new variable, bare soil ratio, representing the fractions of. 2. Theoretical basis for automated soil inference using fuzzy logic Theoretical basis The theoretical basis for soil inference is based on the classic concept of Jenny (, ) that a soil is a product of interaction among climatic factors, landform, parent material, organism, and hydrologi- cal factors over time.
“Differentiation of soil conditions over flat areas using land surface feedback dynamic patterns extracted from MODIS”, Soil Science Society of America Journal. Vol. 74, NO.
3, pp. Vol. 74, NO. 3, pp. Soil is a natural body consisting of layers (soil horizons) resulting from the interplay between climate, topography, organisms, parent material (underlying geologic bedrock), and time.
Thesis: "Soil Pattern Inference Using GIS Under Fuzzy Logic" (Geography), University of Calgary. Thesis: "The White Spruce Communities in the Drumheller Area" (Geography), Beijing Normal University, Beijing, P.
Areas of Specialization. Digital soil mapping requires two basic pieces of information: spatial information on the environmental conditions which co-vary with the soil conditions and the information on relationship between the set of environment covariates and soil conditions.
The former falls into the category of GIS. In the conventional soil survey technique, it is possible to map only 8 soil series atscale, indicating that detail soil mapping is possible by using fuzzy logic. The accuracy of the fuzzy logic derived soil series map was tested using a set of evaluation data.
The result showed an average accuracy of 70%. SoLIM is an automated soil inference system that combines fuzzy logic-based class assignment with a raster GIS representation model, which allows the continuous spatial variation of soils to be expressed at much greater detail so that the class transitions and.
A soil survey procedure using the knowledge of soil pattern established on a previously mapped reference area. Geoderma Use of weights of evidence statistics to define inference rules to disaggregate soil survey maps. In B D. Soil mapping using GIS, expert knowledge, and fuzzy logic.
Soil Science Society of. Groundwater Vulnerability and Risk Mapping Using GIS, Modeling and a Fuzzy Logic Tool. ), which falls under the index category, soil type, the presence/absence of drift and the nature. fuzzy logic and gis 21 Wolfgang Kainz University of Vienna, Austria Rule 2: If the slope is moderate and the aspect is unfavorable and the s now cove r change is.
Evaluation accuracy of soil map was up to %. In conclusion, it is suggested that soil–landscape modeling using FCM and DT methods can be efficiently used as a valuable research technique for spatial soil thickness prediction in a complex soil landscape where soil.
Fuzzy logic specifically addresses situations when the boundaries between classes are not clear. Unlike crisp sets, fuzzy logic is not a matter of in or out of the class; it defines how likely it is that the phenomenon is a member of a set (or class).
Fuzzy logic is based on set theory; therefore, you define possibilities, not probabilities. Under fuzzy logic, a soil at a given pixel (i,j) is represented by a n -element similarity vector: where n is the number of prescribed soil types over the area and is an index which measures the similarity between the local soil at (i,j) to the prescribed soil type k.
is soil type k. Spatial prediction and mapping under GIS; Mapping spatial variation under Third Law of Geography and fuzzy logic using citizen science data (soil mapping, landslide susceptibility mapping, habitat suitability mapping) Spatial sampling (purposive sampling, uncertainty-directed sampling).
An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic. by combining the extracted relationships with the characterized data on predisposing factors through an inference technique developed under fuzzy logic.
SimsonSoil mapping using GIS, expert knowledge, and fuzzy logic. Soil Sci. Soc. Geospatial modeling of surface soil texture of agricultural land using fuzzy logic, geostatistics and GIS techniques. Communications in Soil Science and Plant Analysis: Vol. 50, No. 12, pp.
Fuzzy soil mapping based on prototype category theory Feng Qi a,⁎, A-Xing Zhu b,c, Mark Harrower c, James E. Burt c a Department of Political Science and Geography, University of Texas at San Antonio, NW LoopSan Antonio, TXUSA b State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural.
SoLIM Home | Geography Department Home | UW-Madison Home. Park Street Dept. of Geography, UW-Madison Madison, WI Tel: () Fax: () SoLIM: [email protected] Encode expert knowledge to fuzzy rules Soil inference using fuzzy rules Generate hardened map Generate soil property map References: Zhu, A.X., B.
Hudson, J. Burt, K. Lubich, and D. Simonson, “Soil Mapping Using GIS, Expert Knowledge, and Fuzzy Logic”, Soil Science Society of America Journal. This study aimed to analyse and assess desertification risks in the Upper Phetchaburi River Basin.
Upstream areas are especially crucial for aquatic ecosystems since the mid- and downstream areas are continuously being utilized for agricultural and community purposes. Many parts of the basin have been at moderate risk of drought.
The fuzzy analytical hierarchy process (FAHP) is an effective. In step 5, using the Fuzzy Inference System described each of the indices based on fuzzy logic, and defined the degree of fuzzy membership for each of them, and defined the scope of the index variables in the range 0 and 1 (Neamatollahi et al., a, Neamatollahi et al., b; Akbari et al.,Dang et al., ).
In recent years, fuzzy logic has emerged as a powerful technique in the analysis of hydrologic components and decision making in water resources. Problems related to hydrology often deal with imprecision and vagueness, which can be very well handled by fuzzy logic-based models.
This paper reviews a variety of applications of fuzzy logic in the domain of hydrology and water resources. Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise.
In contrast with binary sets having binary logic, also known as crisp logic, the fuzzy logic variables may have a membership value of not only 0 or as in fuzzy set theory with fuzzy logic the set membership values can range (inclusively) between 0 and. Published under a Creative Commons Attribution International Licence (CC BY ) Developing a fuzzy logic model for predicting soil in˜ltration rate based on soil texture properties Ahmed Z Dewidar 1*, Hussein Al-Ghobari and Abed adaptive neuro-fuzzy inference system, and fuzzy logic system (FLS) approaches (Singh et al., A Soil Land Inference Model (SoLIM) was used to generate membership functions, known as soil similarity vectors (SSVs), as part of the fuzzy logic process (Zhu et al., 15).
Soil similarity vectors were generated at each grid cell location utilizing four terrain attributes, Saga topographic wetness index (TWI), slope, elevation, and modified.Torbert H.A., Krueger E. and Kurtener D. Soil quality assessment using fuzzy modeling. Int. Agrophysics USDA Keys to Soil Taxonomy.
11th edition. Soil Survey Staff, United States Department of Agriculture. p. Van Ranst E., Tang H. Fuzzy reasoning versus Boolean logic in land suitability assessment.