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Desarrollo del sistema LANSAS

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Presentacion sobre el desarrollo del sistema de evaluación de tierras y prediccion de cultivos llamado LANSAS

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Desarrollo del sistema LANSAS

  1. 1. Trent UniversityWatershed Ecosystem Graduated Program A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS Research Proposal
  2. 2. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENTBASED ON FUZZY NEURAL NETWORKSThis research is based on a new approach to landsuitability assessment. It builds on the virtues of fuzzy settheory and parallel processing of data through artificialneural networks. This approach addresses most of theproblems present in current approaches and systems froland evaluation . Trent University Watershed Ecosystem Graduated Program
  3. 3. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENTBASED ON FUZZY NEURAL NETWORKS INTRODUCTION Trent University Watershed Ecosystem Graduated Program
  4. 4. Land Suitability Assessment is the evaluation of performance of the landin terms of the degree of compatibility between the land characteristics(LCh) for a specific land management unit (LMU) and the requirements ofa given Land Utilization Type (LUT) LMU
  5. 5. Land Suitability Assessment is the evaluation of performance of the landin terms of the degree of compatibility between the land characteristics(LCh) for a specific land management unit (LMU) and the requirements ofa given Land Utilization Type (LUT) LCh Climate Soil Socio-economic LMU
  6. 6. Land Suitability Assessment is the evaluation of performance of the landin terms of the degree of compatibility between the land characteristics(LCh) for a specific land management unit (LMU) and the requirements ofa given Land Utilization Type (LUT) LCh Climate Soil Socio-economic LUR Climate LUT Soil Socio-economic LMU
  7. 7. Land Suitability Assessment is the evaluation of performance of the landin terms of the degree of compatibility between the land characteristics(LCh) for a specific land management unit (LMU) and the requirements ofa given Land Utilization Type (LUT) LCh Climate Soil Socio-economic Matching LUR Climate LUT Soil Socio-economic LMU
  8. 8. Land Suitability Assessment is the evaluation of performance of the landin terms of the degree of compatibility between the land characteristics(LCh) for a specific land management unit (LMU) and the requirements ofa given Land Utilization Type (LUT) LCh Climate Soil LUT Suitable Socio-economic Or NOT Suitable on LMU Matching LUR Climate LUT Soil Socio-economic performance LMU
  9. 9. The matching process gives a measure for each LUT evaluated, of the LUT performances in the LMU evaluated. This measure is in terms of suitability classes which are based on the FAO framework for land evaluation. YieldSuitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT Suitability Classification
  10. 10. The matching process gives a measure for each LUT evaluated, of the LUT performances in the LMU evaluated. This measure is in terms of suitability classes which are based on the FAO framework for land evaluation. YieldSuitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT
  11. 11. The matching process gives a measure for each LUT evaluated, of the LUT performances in the LMU evaluated. This measure is in terms of suitability classes which are based on the FAO framework for land evaluation. YieldSuitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT
  12. 12. The matching process gives a measure for each LUT evaluated, of the LUT performances in the LMU evaluated. This measure is in terms of suitability classes which are based on the FAO framework for land evaluation. YieldSuitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT
  13. 13. There are three different and generally-accepted approachesto the Land Suitability Assessment exercise: •Limitations method [FAO, 1984] •Parametric method [Sys, 1985] •Decision trees method [Rossiter, 1986]
  14. 14. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENTBASED ON FUZZY NEURAL NETWORKS PROBLEMS IN CURRENT APPROACHES Trent University Watershed Ecosystem Graduated Program
  15. 15. Current approaches have a number of rather restrictive characteristicsand they have made the land evaluation process a specialized field,inflexible and highly dependent on expert knowledge.LUR data are not available for all kinds of individualsinvolved in land evaluation.
  16. 16. Current approaches have a number of rather restrictive characteristicsand they have made the land evaluation process a specialized field,inflexible and highly dependent on expert knowledge.LUR data are not available for all kinds of individuals involved in landevaluationThe knowledge base used is site-specific
  17. 17. Current approaches have a number of rather restrictive characteristicsand they have made the land evaluation process a specialized field,inflexible and highly dependent on expert knowledge.LUR data are not available for all kind of individuals involved in landevaluation.Knowledge used is site-specificThe accuracy of the assessment varies with thestate of knowledge
  18. 18. Current approaches have a number of rather restrictive characteristicsand they have made the land evaluation process a specialized field,inflexible and highly dependent on expert knowledge.LUR data are not available for all kind of individuals involved in landevaluation.Knowledge used is site-specificAccuracy varies with the state of knowledge LUT performance• Current approaches are based on N S3 S2 S1"crisp" classification systems LCh values
  19. 19. Current approaches have a number of rather restrictive characteristicsand they have made the land evaluation process a specialized field,inflexible and highly dependent on expert knowledge.LUR data are not available for all kind of individuals involved in landevaluation.Knowledge used is site-specificAccuracy varies with the state of knowledge.Current approaches are based on "crisp" classification systems•Current computer systems lack a built-in knowledge base
  20. 20. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENTBASED ON FUZZY NEURAL NETWORKS PROPOSAL OF SOLUTION, HYPOTHESIS AND RESEARCH OBJECTIVES Trent University Watershed Ecosystem Graduated Program
  21. 21. Research ProposalThis research advances a new approach for land suitabilityassessment, which addresses the problems of currentsystems, as defined before.This new approach is based on fuzzy set theory and thevirtues of neural networks. Therefore, a Fuzzy NeuralNetwork (FNN) has been designed in order to automate thisnew approach, which will be translated into a computersystem for land suitability assessment.
  22. 22. HypothesisThe research in this project sets out to test the followinghypothesis:1) Significant gains in accuracy of suitability assessmentresults can be achieved by an interpretive algorithm thatincorporates both, site-specific and universal knowledge. accuracy
  23. 23. The research in this project sets out to test the followinghypothesis:2) The assignment of suitability classes to LMU by fuzzymembership functions produces significant improvements inaccuracy of the assessment of the land than the standardapproaches and algorithms for land suitability assessmentbased on discrete crisp logic . Fuzzy Theory Crisp Theory
  24. 24. The research in this project sets out to test the followinghypothesis: 3) Appropriate knowledge management and fuzzy logic, partof the new approach for land suitability assessment,represent significant improvements in terms of algorithmicefficiency and user accessibility, than the standard computeralgorithm based on decision-trees.
  25. 25. The research in this project sets out to test the followinghypothesis:4) The new paradigm produces superior results in terms ofaccuracy of suitability assessment results than any othercurrent approach for land suitability assessment.
  26. 26. ObjectivesThis research project sets out to achieve the followingobjectives:1) Develop a new paradigm in land suitability assessmentconsisting of a computerized land evaluation system thatovercomes the obstacles and addresses the shortcomingsof the existing manual and automated procedures for landsuitability assessment.
  27. 27. This research project sets out to achieve the followingobjectives:2) this project will explore and examine the virtues of thefuzzy set theory as applied to classifications and transitions,as a potentially fruitful line of inquiry.
  28. 28. This research project sets out to achieve the followingobjectives:3) Explore and examine issues about knowledge management in order to develop a system with the capacity for: a) Providing existing universal knowledge related to LUR b) Providing site-specific knowledge and the capacity for coding new site-specific knowledge of LUR c) Learning and incorporating new knowledge through training with cases where outcomes are known d) Provide users with access to a variety of options involving knowledge and knowledge bases.
  29. 29. This research project sets out to achieve the followingobjectives:4) Examine and explore the virtues and advantage of ANN aspotentially the best approach to implement fuzzy set theory, inorder to determine whether a standard ANN commercially-available software is the most appropriate to theimplementation of the new approach, or whether a softwaresystem should be developed and implemented.
  30. 30. This research project sets out to achieve the followingobjectives:5) Investigate the relative advantages of the systemdeveloped, such as described in objectives 1 to 4, relative tostandard automated land evaluation systems via acomprehensive set of software benchmarking parameters,including user-related concerns.
  31. 31. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENTBASED ON FUZZY NEURAL NETWORKS Research progress And Findings Trent University Watershed Ecosystem Graduated Program
  32. 32. LUR data and Knowledge basesCurrent approaches use two different sources of knowledge about theLUR: • Local Knowledge (SITE DEPENDENT) • Universal Knowledge (NON SITE DEPENDENT)Although, those kind of knowledge are not easy to access and at present,there is no single authoritative source of information on LUTrequirements.However, sufficient information to provide reliable predictions has beenaccumulated from some sources, and they may be consulted to determinethreshold values for land characteristics and limits between suitabilityclasses (FAO, 1986)
  33. 33. Sources Author Knowledge FormatLand evaluation Ir C. Sys Climatic and soil requirements for 21 Survey on different crops. paper The data are ranked by suitability classesEcocrop FAO Climatic and soil requirements for 1700 Digital different species of crops and trees. Database The data are not ranked in suitability classes, but they show the optimal requirements for suitable production.Manual of land R. Ponce; F. Climatic and soil requirements for 72 Survey onevaluation for rainfed Beernaert different crops. paperagriculture The data are ranked by suitability classesSoil legend of the FAO- This is knowledge provide by the 1988 Digitalworld UNESCO FAO Revised Legend, incorporating the Database latest knowledge related to global soil resources and their interrelationshipsClimatological Papadakis It has the knowledge about world climatic Survey onclassification for crops groups, temperature and humidity paper regimes, and suitability and limitations of world climates for some important crops.
  34. 34. Applications of fuzzy set theoryHuajun et al. (1991) Proofed that the approach to the landsuitability assessment based on fuzzy set theory is the mostaccurate, in comparison to the parametric and the limitationmethods. γ LUT performance LUT performance N S3 S2 S1 1 N S3 S2 S1 LCh values α LCh values
  35. 35. However, Huajuns algorithm and its function has beenmodified for the purposes of this research.The algorithm provides fuzzy modeling for only one typeof LUT (corn) and for only one suitability class.Therefore, it was found limiting after testing it with manyLUT and the whole set of suitability classes
  36. 36. LUR or LCh do not fit the pattern of values as predicted byHuajun’s membership function, when applied to them.Actually, the patterns of LUR and LCh found, defined 3different groups of LUR or LCh. 120 1000 100 γ 185 γ γ 220 800 1200 700 850 1050 1450 80 270 750 600 60 325 600 1800 40 α 1345 500 α 500 1801 α2 α1 20 0 0 500 1000 1500 2000
  37. 37. The membership function defined by Huajun can only beapplied to suitability class S1, therefore two extramembership functions should be defined for each of all threesuitability classes (S1, S2, S3) LUT performance 120  0; x ∈ (−∞ , α 1 ) 100   2[( x − α 1 ) /(γ − α 1 )] ; x ∈ [α 1 , β 1 ) 2 S1 80 60 f 1 ( x; α 1 , β 1 , γ ) =  1 − 2[( x − γ ) /(γ − α 1 )] ; x ∈ [ β 1 , γ ) 2 40 20 1; x ∈ [γ , ∞ ) 0  19 20 35 50 51 LUT performance 0; x ∈ (−∞, α 1 ) ∨ x ∈ (α 2 , ∞ ) Yield / performance 120  2[( x − α 1 ) /(γ − α 1 )] ; x ∈ [α 1 , β 1 ) 2 100 2[( x − α ) /(γ − α )] 2 ; x ∈ ( β , α ] 80  f 2 ( x; α 1 , α 2 , β 1 , β 2 , γ ) =  2 2 2 2 S2 60 40 1 − 2[( x − γ ) /(γ − α 1 )] ; x ∈ [ β 1 , γ ) 2 1 − 2[( x − γ ) /(γ − α )] 2 ; x ∈ (γ , β ] 20  2 2 0 1; x = γ  345 325 270 220 185 150 130 110 90 LUT performance 120  0; x ∈ (α 1 , ∞ ) 100   2[( x − γ ) /(γ − α 1 )] ; x ∈ ( β 1 ,α 1 ] 2 80 f 3 ( x;α 1 , β 1 , γ ) =  S3 60 40 1 − 2[( x − α 1 ) /(γ − α 1 )] ; x ∈ (γ , β 1 ] 2 1; x = γ 20  0 0 3 15 35 55
  38. 38. Suitability membership functions changes when they areapplied to each one of the 3 different types of LCh. Therefore, 9different membership functions can be defined from thecombinations of patterns of LCh types and suitability classes. LUT Performance 1 Degree Of membership 0 Values of Land Characteristic type 1 ∞ LUT Performance 1 S1 Degree S2 Of S3 membership 0 Values of Land Characteristic type 2 ∞ 1 LUT Performance Degree Class 3 Of membership 0 Values of Land Characteristic type 3 ∞
  39. 39. Land suitability assessment based on FNNA fuzzy neural network (FNN) has been designed to beapplied primarily to rainfed agriculture, but could be appliedto any land use, provided the knowledge bases (LUR) areavailable. Output layer Output fuzzy layer The FNN proposed, is a typical feed- Conjunction layer forward multilayer perceptron with the 5 layers Fuzzy set nodes Input layer
  40. 40. Membership functions Suitable class LCh 0.6 1938 S1n/N developing stage Membership degree 0.8 0.7731n/N maturation stage Min 0.7731 50Base saturation 155.4 function 2 0.7731 0.7731 MaxOrganic matter kaolinitic 1.2 27 0.00028 functionOrganic matter non kaolinitic 0.8 0.9998 0.7731 0.0408Organic matter calcareous 100 1938 0.9591DepthAnnual rainfall 850 0.0555Length gs 220 155.4 0.00028 0.00028Rainfall gs 800 0.405Mean temperature gs 22 27Mean min. temperature gs 16 0.7731 0.5286 30Mean humidity developing stage 0.00015 0.00028 1938 0.00015slope type 2 (high level) 0.0408 0.0408 2slope type 2 (low level) 99.9 4Coarse fragmentation 155.4CaCO3 6 Centroid Suitability Index 99.9Gypsum 2 27 function 8
  41. 41. Land suitability assessment systemThe approach proposed in this research will be translatedinto a computer software system. It will be known by theacronym “LANSAS” from LANd Suitability AssessmentSystem.LANSAS will be capable of carrying out intensive landsuitability assessments and allowing users to access 3different knowledge bases with information about LUR for awide variety of crops and trees.
  42. 42. The databases and files used in this research are based onFAO guidelines for land evaluation. Data and knowledge are defined and stored in three differenttypes of databases: Thematic, Spatial, Knowledge bases Spatial Databases Sys KB Raster maps databases Vector maps databases Requirements for 21 crops Tables: Climatological Temperature,Rainfall,Humidity Soils KB Solar Radiation,Evaporation KB Topography,Soils Thematic Databases FAO KB Ponce KBLand Management Unit (LMU) Requirements for 1710 crops Requirements for 73 crops Land Utilization Types (LUT) Tables: Tables: Land Characteristics(LC) Climatic zones,Temperature, Temperature,Rainfall,Humidity Socio-economic (SE) Rainfall,Growing period, Light, Solar Radiation,Evaporation Day-length,Soils Topography,Soils,Yields
  43. 43. LANSAS has been designed to use geographic informationfrom standard GIS software (raster and vector). Theinteroperability between LANSAS and GIS software isprovided by Activex controls. LANSAS VBX FNN aXi Activex Objects Map GIS controls ADO DBMS
  44. 44. The capabilities of LANSAS to import-export data fromstandard relational database management systems, are too,an integral part of its design, and this is possible by usingthe Activex controls. LANSAS OCX or Activex Visual Basic Controls Run time OLE Shape-files ODBC Driver ODBC for related Driver tables Image Shape-files Tables Files in Thematic Databases
  45. 45. LANSAS knowledge bases resolve the present problem ofaccessibility to LUR information. Knowledge verification MATCHING Data input S1 GUI S2 Suitable class Spatial S3 Results Land characteristics report and other data is loaded in the neurons Attributes GUI Searching Knowledge in Loaded in Neural KB & DB Network synapses Knowledge bases
  46. 46. The Graphic User Interface (GUI) of LANSAS is friendlyenough to be used by any kind of users.
  47. 47. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENTBASED ON FUZZY NEURAL NETWORKSCurrent state of this research and results Trent University Watershed Ecosystem Graduated Program
  48. 48. •The field research and data gathering are currently inprogress, in order to have the first results in this research.• Knowledge bases have been researched, knowledgecompiled and coded. These knowledge bases are Sysknowledge base, Ponce Knowledge base and FAOknowledge base.•The databases are being designed and implemented on adatabase relational model.
  49. 49. •LANSAS user interface is near completion and somemodules are ready to be used (LANSAS first prototype isnearing completion).•The design of the fuzzy neural network and themembership functions, needed to carry out the landsuitability assessment, have been completed. They will betranslated into code soon.
  50. 50. •An evaluation of the commercially-available neuralnetwork software is in progress, and when completed, themost suitable commercial ANN could be interconnectedwith LANSAS modules.•Two papers have been written and published with topicsrelated directly to this research. One more paper is inpreparation.
  51. 51. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENTBASED ON FUZZY NEURAL NETWORKS Conclusion Trent University Watershed Ecosystem Graduated Program
  52. 52. ConclusionIt is expected that the following conclusions could bereached at the end of this research:The approach based on Fuzzy Neural Networks is superior tocurrent approaches for land suitability assessment. Thecomputer implementation of this approach, called “LANSAS”,offers a host of new capabilities to users that are notavailable in current land evaluation systems: • Knowledge accessibility • GIS Interface • User friendly and faster • Portability • Based on goodness of Neural Networks • Interoperability
  53. 53. LAND SUITABILITY APPROACHESDIGITAL KNOWLEDGE BASESDIGITAL DATABASESFUZZY NEURAL NETWORK FOR LSALANSAS CICLE OF LIFETEST AND VALIDATION
  54. 54. Limitation MethodThe limitation methods expresses the land conditions in a relativescale, such limitations are derivations from the optimal conditions ofa LC which adversely affect a kind of land use.If a LC is optimal for a crop growth it has no limitations; at the otherhand, when the same LC is unfavorable for crop growth, it has severelimitations.The evaluation is realized in several degrees of limitation. 5 level scalein the range of degree of limitations.
  55. 55. Limitation Methodscale Class limitation description level 0 S1 no limitation LC is optimal for LUT 1 S1 slight LC is nearly optimal for LUT and affect the limitation productivity for not more than 20% 2 S2 moderate LC has moderate influence on yield limitation decrease. Benefit can still be made and use of land remains profitable 3 S3 severe LC has such an influence on productivity of limitation the land that the use becomes marginal for the considered LUT 4 N1 very severe Such limitations will not only decrease the and limitation yield below the profitable level but even may N2 totally inhibit the use of the soil for the considered LUT
  56. 56. Limitation Method LUT S1 S1 S2 S3 N1 N2 requirements LC 0 1 2 3 4 4Annual rainfall 1600 to 2000 2000 to 2500 1400 to 1600 1200 to 1400 <1200 2500 to 3000 3000 to 3500 >3500Length dry <1 1 to 2 2 to 3 3 to 4 >4seasonMean annual 25 to 28 23 to 25 22 to 23 21 to 22 < 21Temperature 28 to 32 32 to 35 35 to 38 > 38Mean relative 45 to 60 45 to 40 40 to 35 35 to 30 < 30humidity 60 to 65 65 to 75 75 to 85 > 85 LC of LMU value Limitatio n level LC limitations are characterized by 2 optimal, 1 slight and 1 moderateAnnual rainfall 1862 0 limitations.Length dry season 0 0 Therefore the suitability class is S2Mean annual 24.9 1TemperatureMean relative humidity 75 2
  57. 57. Parametric MethodThis approach consists in a numeral rating of the different limitationlevels of LC in a scale from 100 to a minimum value.If a LC is optimal for the considered LUT the maximal rating of 100 isattributed; If the same LC is unfavourable a minimal rating is applied.The rating of the different LC are finally multiplied in order to obtain aland index. Clas Index for s parametric method Index = A * (B/100) * (C/100) * .... S1 75 to 100 S2 50 to 75 S3 25 to 50 N 0 to 25
  58. 58. Parametric Method LUT S1 S1 S2 S3 N1 N2 requirements LC 100 75 50 25 0Annual rainfall 1600 to 2000 2000 to 2500 1400 to 1600 1200 to 1400 <1200 2500 to 3000 3000 to 3500 >3500Length dry <1 1 to 2 2 to 3 3 to 4 >4seasonMean annual 25 to 28 23 to 25 22 to 23 21 to 22 < 21Temperature 28 to 32 32 to 35 35 to 38 > 38Mean relative 45 to 60 45 to 40 40 to 35 35 to 30 < 30humidity 60 to 65 65 to 75 75 to 85 > 85 LC of LMU value RatingAnnual rainfall 1862 100Length dry season 0 100 Index = 100 *(95/100) * (60/100) = 57Mean annual 24.9 95Temperature Suitability class = S2Mean relative humidity 75 60
  59. 59. Decision Trees MethodOne of the current paradigms for land evaluation uses decision trees tocarry out the assessment and it has been coded into a computer softwareprogram called the Automated Land Evaluation System, or “ALES”(Rossiter, 1986). "ALES" is a PC computer program shell that evaluators can use to buildtheir own pseudo-expert systems taking into account local conditions."ALES" is not by itself an expert system, and the knowledge about landand land use contained in “ALES” are coded in decision-trees from.The “ALES” has a dBase interface and can be linked to GIS systems suchas ARC/INFO and IDRISI.
  60. 60. Land Characteristic Value Precipitation 1500 Length Growing Period 130 Temperature 33 <500 [500, 1000) [1000, 1500) 1500 (1500, 2000] (2000, 3000]Precipitation N2 S3 S2 S1 S2 S3 >3000 N2 [100, 150) 150 S2 S1 [50, 100) S3 Land Characteristic Clas Length Growing Period s Precipitation S1 Length Growing S2 25 S1 Period Temperature S3 Temperature [22,25) (25, 30] S2 S2 (30, 35] S3 Suitability Class S3 >35 N2
  61. 61. Digital Knowledge BasesAt present, there is no single authoritative source ofinformation on LUT requirements.However, some sources with sufficient information toprovide reliable predictions has been accumulated, andthey may be consulted to determine threshold values forland characteristics and limits between suitabilityclasses.Above conclusion was inferred after an intensive search performedabout to LUT requirements in scientific surveys, agricultureliterature, official institutions involved in agricultural research (FAO,USDA, etc.), and some specific web sites.
  62. 62. Digital Knowledge BasesAlso, the search allowed finding a data set of LUT requirements andsome information in relation of them.Data found consisting of a lists of soil and climatic requirements forwide range of different crops, types of soils, and climatic classes.The data collected were stored in 5 different digital knowledge bases. Crop, Requirement, LC-Type, threshold values Soil series Climatic classes Ponce/Beernaert FAO-UNESCO Papadakis Ecocrop-FAO Sys Soil legend
  63. 63. Digital Knowledge Bases Sources Author Knowledge FormatLand evaluation Ir C. Sys Climatic and soil requirements for 21 Survey on different crops. paper The data are ranked by suitability classesEcocrop FAO Climatic and soil requirements for 1700 Digital different species of crops and trees. Database The data are not ranked in suitability classes, but they show the optimal requirements for suitable production.Manual of land Raul Ponce; Climatic and soil requirements for 72 Survey onevaluation for rainfed F. Beernaert different crops. paperagriculture The data are ranked by suitability classesSoil legend of the FAO- This is knowledge provide by the 1988 Digitalworld UNESCO FAO Revised Legend, incorporating the Database latest knowledge related to global soil resources and their interrelationshipsClimatological Papadakis It has the knowledge about world climatic Survey onclassification for crops groups, temperature and humidity paper regimes, and suitability and limitations of world climates for some important crops.
  64. 64. Digital Knowledge BasesA knowledge base is a declarative representation of knowledge,usually originating from the expert and represented in terms of rules.Therefore, the knowledge bases created in this research are notknowledge bases in the strictest sense, and they are only a set oftabular structures.However, these tables have knowledge accumulated in terms of croprequirements, soils classes, climatic types, and crop yield indexescreated, gathered and tested by different experts, but they are notrepresented as rules.But, those tables will be called knowledge bases in this research forconvenience.
  65. 65. Digital Databases Definition for Land Suitability Assessment The enterprise model (Date, 1995) used, in this research has an emphasis on crop production under rainfed agriculture. Thus, the databases and files used in this research should be based on the following criteria: •LUT definition and their characteristics. •LMU definition and their characteristics. •Crops definition and their requirements. •Crops production, their characteristics and components. •Spatial distribution to of each of the above instances. This research has three different types of databases: •Thematic or Attributes databases •Spatial databases •Knowledge bases
  66. 66. Digital Databases Definition for Land Suitability Assessment Thematic Databases: These databases contain the description and components of the each entity and their attributes and relations. These databases may have a strong relationship with the spatial databases, because these may have the whole description of the each spatial entity. Some important tables into these database are: •PROJECT •EVALUATION •LMU •LMU DESCRIPTION •LUT •LUT DESCRIPTION •LCh •LCh VALUES •CROP GROWN
  67. 67. Digital Databases Definition for Land Suitability Assessment •Spatial Databases: These databases store the graphic primitives that make up objects representing entities. The contextual relationships between graphic primitives from the topological tables. These are required for the GIS and they can be used for the two different spatial models of data, raster and vector. This databases are: oThe shapes databases or shapes files for vector maps oImages databases or images files for raster maps oDatabase of coordinate system oGeo-reference database oDocumentation files for the images and map files
  68. 68. Digital Databases Definition for Land Suitability AssessmentKnowledge bases: Although these are not really knowledge bases inthe strict definition, these databases can be called knowledge basesbecause they contain the information held by the human expert.They will be used for the artificial neural network. These knowledgebases are about:oCrops (1700 different types of crops and trees )oCrop requirements ( three knowledge bases )oClimatic classes ( Papadakis climatic classification )oSoil type ( world map of FAO/UNESCO )oSuitability index of crops ( Based on FAO Land Evaluation Framework )
  69. 69. Sys Knowledge base Requirements for 21 crops Tables: User Temperature,Rainfall,Humidity Thematic Databases Solar Radiation,Evaporation Interface Topography,Soils Tables: Ponce Knowledge baseLand Management Unit (LMU) Land Utilization Types (LUT) Requirements for 73 crops Land Characteristics(LC) Tables: Socio-economic (SE) Temperature,Rainfall,Humidity Interoperability available by Solar Radiation,Evaporation Spatial Databases Programming code, ODBC, Topography,Soils,Yields Or Raster maps databases Activex FAO Knowledge base Vector maps databases Requirements for 1710 crops Tables: Climatic zones,Temperature, Evaluation Rainfall,Growing period, Light, Day-length,Soils Modules Soil Climatological Classification classification
  70. 70. Land Suitability Assessment based on FNNFuzzy systems and artificial neural networks have similarcharacteristics and are complementary to each other.Their complementary parallelism properties have led researchers tocombine them into the called neuro-fuzzy systems or fuzzy neuralnetworks, which are more suitable for complex and impreciselydefined applications.In fuzzy logic, a linguistic variable like "suitable" can have severallinguistic values like "very", "moderate" or "marginal".Each linguistic value is viewed as a fuzzy set associated with amembership function, which can be triangular, bell-shaped, or of anyother form (Fu, 1994).
  71. 71. Land Suitability Assessment based on FNN Output layer Output fuzzy layer Conjunction layer Fuzzy set nodes Input layer
  72. 72. Land Suitability Assessment based on FNNInput layer: There are the input values for each one of the three differentclasses of LC and there values are taken with the user enter all of theparameters of a given LMU to be evaluated. The minimum data should beentered by the user is: oLUT name oLand characteristic name oLand characteristic value in a given land unit to be evaluated oLand Characteristic Type oName of the land management unit to be assessedWhen the user gives the crop name, land characteristic and the value, thenthe network is activated to start the evaluation. The activation level of eachinput unit is the value of the LC (Xi) in the given instance, thus, there is not anactivation function.
  73. 73. Land Suitability Assessment based on FNN
  74. 74. Land Suitability Assessment based on FNNThe input fuzzy layer: In this layer the input data from the previouslayer are processed by the summation function, and then the resultof this function is evaluated into a membership function (anyone ofthe 9 membership functions defined in this research).In this layer, in order to use the membership function, some valueshave to be calculated, these values are from the knowledge basesdefined in this research, such values are: γ,α i and β i.γ is the most suitable value of a crop requirement for the LUT given,α is the most marginal suitable value of a crop requirement for theLUT given, and β is a medium mean between γ and α, it can becalculated by β=(γ+α)/2
  75. 75. Land Suitability Assessment based on FNN θ 2=0 Neuron i,2 in n conjunction Layer Oi , 2 = f1 (∑ X iWi , 2 − θ 2 ) i =1 Wi,2=1  0; xi ∈ (− ∞ ,α 1 )xi  S1  2[( xi − α 1 ) /(γ − α 1 )] ; xi ∈ [α 1 , β 1 ) 2 Oi,2 f1 ( xi ;α 1 , β 1 , γ ) =   1 − 2[( xi − γ ) /(γ − α 1 )]2 ; xi ∈ [ β 1 , γ ) Wi,2=1  1; x ∈ [γ , ∞ )  i Neuron i,1 in Input Fuzzy Layer
  76. 76. Land Suitability Assessment based on FNNConjunction layer: the conjunction units take the minimum value of thedegrees of the each one of the suitability classes.The activation function in this layer is the min function, this functionselects the minimum value from a vector given, It has sense, becauseon the definition of FAO limitations method, the suitable class will beattributed, to the land characteristics compared with the croprequirements, according to the less favorable land characteristic (Sys,1985).The conjunction layer says how the neural network takes the decisionabout which is the beast option for a given input.
  77. 77. Land Suitability Assessment based on FNN θ 3=0 Neuron i,3 in Output Fuzzy Layer S Oi,3 Wi,3=1 n Wi,3=1Oi,2 Oi ,3 = min(∑ Oi , 2Wi ,3 − θ 3 ) i =1 Oi,4 Wi,4 Neuron i,2 in Conjunction Layer O Neuron i,4 in Output Layer
  78. 78. Land Suitability Assessment based on FNNOutput fuzzy layer: The output fuzzy set units collect the informationfrom the conjunction units (each corresponding to a fuzzy rule).The output fuzzy set units use the max function as activationfunction because the grade of membership of an object based on allfuzzy rules is given for this function.Other important step in the fuzzy systems, the defuzzification, onlycan be reach by the use of the max function at first, and follows bythe centroid method (Fu, 1994).
  79. 79. Land Suitability Assessment based on FNN O1,3 W1,3=1 n O2,3 S = max(∑ Oi ,3Wi , 4 − θ 4 ) Membership degree in i =1 Suitability class obtained W2,3=1W1,4=100O1,3O3,3W3,3=1 n nW2,4=60 ∑O ∑ X W i ,3 i i,2 −θ 2 Suitability Index in O= i =1 i =1 n Suitability class obtainedO2,3 ∑O i =1 i ,3 W3,4=40 O3,3
  80. 80. Land Suitability Assessment based on FNNOutput layer: The output units generate the final result byintegrating the information from the output fuzzy set units.This units calculate its activation level on the defuzzificationmethod, this method refers to translating the membership grades ofa fuzzy set into a crisp value, and it is centroid method.The centroid method calculates the crisp value from a given variablegiven, its calculation finds the centroid/center of gravity of theregion bounded by the output membership function of the outputfuzzy set units.
  81. 81. Suitable class 1938 S1 0.6 Membership degreen/N developing stage 0.7731 0.8n/N maturation stage 50 155.4 0.7731Base saturation 2Organic matter kaolinitic 0.7731 0.7731 1.2 27 0.00028Organic matter non kaolinitic 0.9998 0.7731 0.8 0.0408Organic matter calcareous 100 0.9591 1938Depth 850Annual rainfall 220 0.0555Length gs 800 155.4 0.00028 0.00028Rainfall gs 0.405 22Mean temperature gs 27 16Mean min. temperature gs 0.7731 30 0.5286Mean humidity developing stage 0.0408 0.00028 1938 0.00015 2 0.0408slope type 2 (high level) 0.0408slope type 2 (low level) 4 99.9Coarse fragmentation 6 155.4CaCO3 2 Suitability Index 8 27 99.9Gypsum
  82. 82. The approach proposed here could be translated into a computersoftware called LANSAS.LANSAS must be capable to carry out intensive land suitabilityassessments, and allowing users to access to 3 different knowledgebases with data about LUT requirements for a wide variety of cropsfor the most grown under rainfed agriculture.Additionally, LANSAS will be design as to use geographicinformation from standard GIS software (raster and vector). Thecapabilities of LANSAS to import-export data from standard relationaldatabases management systems, are too part of its design.The user interface of LANSAS could be friendly enough to be used byany kind of users. And its knowledge bases resolve the old problemof lack of about to not accessibility of this kind of information.
  83. 83. The computer system will be designed, and developed adopting theprototyping paradigm from the software engineer perspective.This is convenient, because the development process time is short,and it can provide dramatic savings in total software life cycle costs[Isensee, 1996]. List of List of List of revisions revisions revisions Revise User/designer prototype review Prototype Prototype Prototype Test requirements design system System requirements (sometimes informal or incomplete) Delivered system
  84. 84. Field Trials Ecozone 1 2 plots with corn Ecozone 3 4 plots 2 with corn and 2 with beans Ecozone 2 4 plots Ecozone 4 2 with corn and 4 plots 2 with beans 2 with corn and 2 with beansSteps for select field trials:1: Land Management Units or Ecozones definition (4 in whole area of the watershed)2: To Select 2 LUT (LUT1: corn, LUT2: beans ) and setting up 4 plots in each LMU or EZ.3: To carry out analysis of accuracy of prediction (to assess the 3 algorithms)
  85. 85. Reality Field work Yield production = 10 ton. per ha. Test of accuracy System 1: Comparison against reality Parametric method Class S1 2: Benchmarking algorithms Yield productionprediction = 11 Ton. Per ha. LANSAS Class S1 Yield production prediction = 9 Ton. Per Ha. Decision-Trees Class S3 Yield production prediction = 5 Ton. Per Ha. 4 plots with 2 different LUT (corn, beans) within 4 different LMU
  86. 86. Source of Degrees of freedom Sum of squares Mean squares F calculated Ft variation DF SS MS Fc 5% SV 1% 2 B Blocks r–1 r SSB/(r-1) MSB/MSE ∑ r i −G ∑ i =1 j MSB Treatments t–1 2 SST/(t-1) MST/MSE t T ∑ j −G MST j =1 rr j Error SSE = ST-SSB-SST SSE/Dfe t r ∑ rj − r − t + 1 MSE j =1 t ∑Y Total r ∑ rj − 1 2 ij −G j =1 ijWhere: r= repetitions in blocks; G= correction factor represented by G=(Y2)/(r Σ t j=1 rj); Bi=β i;Tj= τ j;
  87. 87. Blocks LANSAS LANSAS Yield Real yield Real yield Bi Yield predicted for in field site in field site predicted for corn site 2 1 2 corn site 1 Ecozone 1 48 tons. 48 tons. 50 tons. 47 tons. 193 Ecozone 2 56 tons. 56 tons. 55 tons. 56.5 tons. 223.5 Ecozone 3 34 tons. 34 tons. 33.5 tons 33.75 135.25 Ecozone 4 66 tons. 66 tons. 67 tons. 64 tons. 263 Y=814. 75example of the experimental design applied in the Texcoco river watershed to test level of accuracyprediction between LANSAS and other current paradigms in land suitability assessment.
  88. 88. Treatments rj rrj Tj MeanLansas 4 8 408 51Reality 4 8 406.7 50.8Sum 8 16 814.7 50.9 Example of results obtained from completely randomized blocks method

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