![There are three different and generally-accepted approaches
to the Land Suitability Assessment exercise:
•Limitations method [FAO, 1984]
•Parametric method [Sys, 1985]
•Decision trees method [Rossiter, 1986]](https://image.slidesharecdn.com/qexamination-120828112745-phpapp02/75/Desarrollo-del-sistema-LANSAS-13-2048.jpg)
![The membership function defined by Huajun can only be
applied to suitability class S1, therefore two extra
membership functions should be defined for each of all three
suitability 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](https://image.slidesharecdn.com/qexamination-120828112745-phpapp02/75/Desarrollo-del-sistema-LANSAS-37-2048.jpg)
![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](https://image.slidesharecdn.com/qexamination-120828112745-phpapp02/75/Desarrollo-del-sistema-LANSAS-60-2048.jpg)
![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](https://image.slidesharecdn.com/qexamination-120828112745-phpapp02/75/Desarrollo-del-sistema-LANSAS-75-2048.jpg)
![The computer system will be designed, and developed adopting the
prototyping 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](https://image.slidesharecdn.com/qexamination-120828112745-phpapp02/75/Desarrollo-del-sistema-LANSAS-83-2048.jpg)
Uploaded byLuis Manuel Callejas
Desarrollo del sistema LANSAS
The research proposal outlines a new computer system for land suitability assessment that utilizes fuzzy set theory and artificial neural networks to address the limitations of existing methods in evaluating land compatibility for specific management practices. It aims to improve the accuracy and efficiency of land suitability assessment by integrating site-specific and universal knowledge, ultimately creating a system known as 'lansas' that can conduct comprehensive evaluations and provide users with accessible information on various crops. Current data collection and field research are ongoing to support the development of this system.
More Related Content
Similar to Desarrollo del sistema LANSAS
Desarrollo del sistema LANSAS
- 1. Trent University Watershed EcosystemGraduated Program A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS Research Proposal
- 2. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS This research is based on a new approach to land suitability assessment. It builds on the virtues of fuzzy set theory and parallel processing of data through artificial neural networks. This approach addresses most of the problems present in current approaches and systems fro land evaluation . Trent University Watershed Ecosystem Graduated Program
- 3. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS INTRODUCTION Trent University Watershed Ecosystem Graduated Program
- 4. Land Suitability Assessmentis the evaluation of performance of the land in terms of the degree of compatibility between the land characteristics (LCh) for a specific land management unit (LMU) and the requirements of a given Land Utilization Type (LUT) LMU
- 5. Land Suitability Assessmentis the evaluation of performance of the land in terms of the degree of compatibility between the land characteristics (LCh) for a specific land management unit (LMU) and the requirements of a given Land Utilization Type (LUT) LCh Climate Soil Socio-economic LMU
- 6. Land Suitability Assessmentis the evaluation of performance of the land in terms of the degree of compatibility between the land characteristics (LCh) for a specific land management unit (LMU) and the requirements of a given Land Utilization Type (LUT) LCh Climate Soil Socio-economic LUR Climate LUT Soil Socio-economic LMU
- 7. Land Suitability Assessmentis the evaluation of performance of the land in terms of the degree of compatibility between the land characteristics (LCh) for a specific land management unit (LMU) and the requirements of a given Land Utilization Type (LUT) LCh Climate Soil Socio-economic Matching LUR Climate LUT Soil Socio-economic LMU
- 8. Land Suitability Assessmentis the evaluation of performance of the land in terms of the degree of compatibility between the land characteristics (LCh) for a specific land management unit (LMU) and the requirements of a 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. The matching processgives 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. Yield Suitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT Suitability Classification
- 10. The matching processgives 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. Yield Suitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT
- 11. The matching processgives 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. Yield Suitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT
- 12. The matching processgives 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. Yield Suitability Classification Very suitable Moderately suitable Marginally suitable Non suitable 0 LCh performance on LUT
- 13. There are threedifferent and generally-accepted approaches to the Land Suitability Assessment exercise: •Limitations method [FAO, 1984] •Parametric method [Sys, 1985] •Decision trees method [Rossiter, 1986]
- 14. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS PROBLEMS IN CURRENT APPROACHES Trent University Watershed Ecosystem Graduated Program
- 15. Current approaches havea number of rather restrictive characteristics and 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 land evaluation.
- 16. Current approaches havea number of rather restrictive characteristics and 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 land evaluation The knowledge base used is site-specific
- 17. Current approaches havea number of rather restrictive characteristics and 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 land evaluation. Knowledge used is site-specific The accuracy of the assessment varies with the state of knowledge
- 18. Current approaches havea number of rather restrictive characteristics and 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 land evaluation. Knowledge used is site-specific Accuracy varies with the state of knowledge LUT performance • Current approaches are based on N S3 S2 S1 "crisp" classification systems LCh values
- 19. Current approaches havea number of rather restrictive characteristics and 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 land evaluation. Knowledge used is site-specific Accuracy varies with the state of knowledge. Current approaches are based on "crisp" classification systems •Current computer systems lack a built- in knowledge base
- 20. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS PROPOSAL OF SOLUTION, HYPOTHESIS AND RESEARCH OBJECTIVES Trent University Watershed Ecosystem Graduated Program
- 21. Research Proposal This researchadvances a new approach for land suitability assessment, which addresses the problems of current systems, as defined before. This new approach is based on fuzzy set theory and the virtues of neural networks. Therefore, a Fuzzy Neural Network (FNN) has been designed in order to automate this new approach, which will be translated into a computer system for land suitability assessment.
- 22. Hypothesis The research inthis project sets out to test the following hypothesis: 1) Significant gains in accuracy of suitability assessment results can be achieved by an interpretive algorithm that incorporates both, site-specific and universal knowledge. accuracy
- 23. The research inthis project sets out to test the following hypothesis: 2) The assignment of suitability classes to LMU by fuzzy membership functions produces significant improvements in accuracy of the assessment of the land than the standard approaches and algorithms for land suitability assessment based on discrete crisp logic . Fuzzy Theory Crisp Theory
- 24. The research inthis project sets out to test the following hypothesis: 3) Appropriate knowledge management and fuzzy logic, part of the new approach for land suitability assessment, represent significant improvements in terms of algorithmic efficiency and user accessibility, than the standard computer algorithm based on decision-trees.
- 25. The research inthis project sets out to test the following hypothesis: 4) The new paradigm produces superior results in terms of accuracy of suitability assessment results than any other current approach for land suitability assessment.
- 26. Objectives This research projectsets out to achieve the following objectives: 1) Develop a new paradigm in land suitability assessment consisting of a computerized land evaluation system that overcomes the obstacles and addresses the shortcomings of the existing manual and automated procedures for land suitability assessment.
- 27. This research projectsets out to achieve the following objectives: 2) this project will explore and examine the virtues of the fuzzy set theory as applied to classifications and transitions, as a potentially fruitful line of inquiry.
- 28. This research projectsets out to achieve the following objectives: 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. This research projectsets out to achieve the following objectives: 4) Examine and explore the virtues and advantage of ANN as potentially the best approach to implement fuzzy set theory, in order to determine whether a standard ANN commercially- available software is the most appropriate to the implementation of the new approach, or whether a software system should be developed and implemented.
- 30. This research projectsets out to achieve the following objectives: 5) Investigate the relative advantages of the system developed, such as described in objectives 1 to 4, relative to standard automated land evaluation systems via a comprehensive set of software benchmarking parameters, including user-related concerns.
- 31. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS Research progress And Findings Trent University Watershed Ecosystem Graduated Program
- 32. LUR data andKnowledge bases Current approaches use two different sources of knowledge about the LUR: • 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 LUT requirements. However, sufficient information to provide reliable predictions has been accumulated from some sources, and they may be consulted to determine threshold values for land characteristics and limits between suitability classes (FAO, 1986)
- 33. Sources Author Knowledge Format Land evaluation Ir C. Sys Climatic and soil requirements for 21 Survey on different crops. paper The data are ranked by suitability classes Ecocrop 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 on evaluation for rainfed Beernaert different crops. paper agriculture The data are ranked by suitability classes Soil legend of the FAO- This is knowledge provide by the 1988 Digital world UNESCO FAO Revised Legend, incorporating the Database latest knowledge related to global soil resources and their interrelationships Climatological Papadakis It has the knowledge about world climatic Survey on classification for crops groups, temperature and humidity paper regimes, and suitability and limitations of world climates for some important crops.
- 34. Applications of fuzzyset theory Huajun et al. (1991) Proofed that the approach to the land suitability assessment based on fuzzy set theory is the most accurate, in comparison to the parametric and the limitation methods. γ LUT performance LUT performance N S3 S2 S1 1 N S3 S2 S1 LCh values α LCh values
- 35. However, Huajun's algorithmand its function has been modified for the purposes of this research. The algorithm provides fuzzy modeling for only one type of LUT (corn) and for only one suitability class. Therefore, it was found limiting after testing it with many LUT and the whole set of suitability classes
- 36. LUR or LChdo not fit the pattern of values as predicted by Huajun’s membership function, when applied to them. Actually, the patterns of LUR and LCh found, defined 3 different 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. The membership functiondefined by Huajun can only be applied to suitability class S1, therefore two extra membership functions should be defined for each of all three suitability 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. Suitability membership functionschanges when they are applied to each one of the 3 different types of LCh. Therefore, 9 different membership functions can be defined from the combinations 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. Land suitability assessmentbased on FNN A fuzzy neural network (FNN) has been designed to be applied primarily to rainfed agriculture, but could be applied to any land use, provided the knowledge bases (LUR) are available. 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. Membership functions Suitable class LCh 0.6 1938 S1 n/N developing stage Membership degree 0.8 0.7731 n/N maturation stage Min 0.7731 50 Base saturation 155.4 function 2 0.7731 0.7731 Max Organic matter kaolinitic 1.2 27 0.00028 function Organic matter non kaolinitic 0.8 0.9998 0.7731 0.0408 Organic matter calcareous 100 1938 0.9591 Depth Annual rainfall 850 0.0555 Length gs 220 155.4 0.00028 0.00028 Rainfall gs 800 0.405 Mean temperature gs 22 27 Mean min. temperature gs 16 0.7731 0.5286 30 Mean humidity developing stage 0.00015 0.00028 1938 0.00015 slope type 2 (high level) 0.0408 0.0408 2 slope type 2 (low level) 99.9 4 Coarse fragmentation 155.4 CaCO3 6 Centroid Suitability Index 99.9 Gypsum 2 27 function 8
- 41. Land suitability assessmentsystem The approach proposed in this research will be translated into a computer software system. It will be known by the acronym “LANSAS” from LANd Suitability Assessment System. LANSAS will be capable of carrying out intensive land suitability assessments and allowing users to access 3 different knowledge bases with information about LUR for a wide variety of crops and trees.
- 42. The databases andfiles used in this research are based on FAO guidelines for land evaluation. Data and knowledge are defined and stored in three different types 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 KB Land 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. LANSAS has beendesigned to use geographic information from standard GIS software (raster and vector). The interoperability between LANSAS and GIS software is provided by Activex controls. LANSAS VBX FNN aXi Activex Objects Map GIS controls ADO DBMS
- 44. The capabilities ofLANSAS to import-export data from standard relational database management systems, are too, an integral part of its design, and this is possible by using the 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. LANSAS knowledge basesresolve the present problem of accessibility 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. The Graphic UserInterface (GUI) of LANSAS is friendly enough to be used by any kind of users.
- 47. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS Current state of this research and results Trent University Watershed Ecosystem Graduated Program
- 48. •The field researchand data gathering are currently in progress, in order to have the first results in this research. • Knowledge bases have been researched, knowledge compiled and coded. These knowledge bases are Sys knowledge base, Ponce Knowledge base and FAO knowledge base. •The databases are being designed and implemented on a database relational model.
- 49. •LANSAS user interfaceis near completion and some modules are ready to be used (LANSAS first prototype is nearing completion). •The design of the fuzzy neural network and the membership functions, needed to carry out the land suitability assessment, have been completed. They will be translated into code soon.
- 50. •An evaluation ofthe commercially-available neural network software is in progress, and when completed, the most suitable commercial ANN could be interconnected with LANSAS modules. •Two papers have been written and published with topics related directly to this research. One more paper is in preparation.
- 51. A COMPUTER SYSTEM FOR LAND SUITABILITY ASSESSMENT BASED ON FUZZY NEURAL NETWORKS Conclusion Trent University Watershed Ecosystem Graduated Program
- 52. Conclusion It is expectedthat the following conclusions could be reached at the end of this research: The approach based on Fuzzy Neural Networks is superior to current approaches for land suitability assessment. The computer implementation of this approach, called “LANSAS”, offers a host of new capabilities to users that are not available in current land evaluation systems: • Knowledge accessibility • GIS Interface • User friendly and faster • Portability • Based on goodness of Neural Networks • Interoperability
- 53. LAND SUITABILITY APPROACHES DIGITALKNOWLEDGE BASES DIGITAL DATABASES FUZZY NEURAL NETWORK FOR LSA LANSAS CICLE OF LIFE TEST AND VALIDATION
- 54. Limitation Method The limitationmethods expresses the land conditions in a relative scale, such limitations are derivations from the optimal conditions of a LC which adversely affect a kind of land use. If a LC is optimal for a crop growth it has no limitations; at the other hand, when the same LC is unfavorable for crop growth, it has severe limitations. The evaluation is realized in several degrees of limitation. 5 level scale in the range of degree of limitations.
- 55. Limitation Method scale 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. Limitation Method LUT S1 S1 S2 S3 N1 N2 requirements LC 0 1 2 3 4 4 Annual rainfall 1600 to 2000 2000 to 2500 1400 to 1600 1200 to 1400 <1200 2500 to 3000 3000 to 3500 >3500 Length dry <1 1 to 2 2 to 3 3 to 4 >4 season Mean annual 25 to 28 23 to 25 22 to 23 21 to 22 < 21 Temperature 28 to 32 32 to 35 35 to 38 > 38 Mean relative 45 to 60 45 to 40 40 to 35 35 to 30 < 30 humidity 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 moderate Annual rainfall 1862 0 limitations. Length dry season 0 0 Therefore the suitability class is S2 Mean annual 24.9 1 Temperature Mean relative humidity 75 2
- 57. Parametric Method This approachconsists in a numeral rating of the different limitation levels 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 is attributed; If the same LC is unfavourable a minimal rating is applied. The rating of the different LC are finally multiplied in order to obtain a land 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. Parametric Method LUT S1 S1 S2 S3 N1 N2 requirements LC 100 75 50 25 0 Annual rainfall 1600 to 2000 2000 to 2500 1400 to 1600 1200 to 1400 <1200 2500 to 3000 3000 to 3500 >3500 Length dry <1 1 to 2 2 to 3 3 to 4 >4 season Mean annual 25 to 28 23 to 25 22 to 23 21 to 22 < 21 Temperature 28 to 32 32 to 35 35 to 38 > 38 Mean relative 45 to 60 45 to 40 40 to 35 35 to 30 < 30 humidity 60 to 65 65 to 75 75 to 85 > 85 LC of LMU value Rating Annual rainfall 1862 100 Length dry season 0 100 Index = 100 *(95/100) * (60/100) = 57 Mean annual 24.9 95 Temperature Suitability class = S2 Mean relative humidity 75 60
- 59. Decision Trees Method Oneof the current paradigms for land evaluation uses decision trees to carry out the assessment and it has been coded into a computer software program called the Automated Land Evaluation System, or “ALES” (Rossiter, 1986). "ALES" is a PC computer program shell that evaluators can use to build their own pseudo-expert systems taking into account local conditions. "ALES" is not by itself an expert system, and the knowledge about land and land use contained in “ALES” are coded in decision-trees from. The “ALES” has a dBase interface and can be linked to GIS systems such as ARC/INFO and IDRISI.
- 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. Digital Knowledge Bases Atpresent, there is no single authoritative source of information on LUT requirements. However, some sources with sufficient information to provide reliable predictions has been accumulated, and they may be consulted to determine threshold values for land characteristics and limits between suitability classes. Above conclusion was inferred after an intensive search performed about to LUT requirements in scientific surveys, agriculture literature, official institutions involved in agricultural research (FAO, USDA, etc.), and some specific web sites.
- 62. Digital Knowledge Bases Also,the search allowed finding a data set of LUT requirements and some information in relation of them. Data found consisting of a lists of soil and climatic requirements for wide 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. Digital Knowledge Bases Sources Author Knowledge Format Land evaluation Ir C. Sys Climatic and soil requirements for 21 Survey on different crops. paper The data are ranked by suitability classes Ecocrop 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 on evaluation for rainfed F. Beernaert different crops. paper agriculture The data are ranked by suitability classes Soil legend of the FAO- This is knowledge provide by the 1988 Digital world UNESCO FAO Revised Legend, incorporating the Database latest knowledge related to global soil resources and their interrelationships Climatological Papadakis It has the knowledge about world climatic Survey on classification for crops groups, temperature and humidity paper regimes, and suitability and limitations of world climates for some important crops.
- 64. Digital Knowledge Bases Aknowledge 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 not knowledge bases in the strictest sense, and they are only a set of tabular structures. However, these tables have knowledge accumulated in terms of crop requirements, soils classes, climatic types, and crop yield indexes created, gathered and tested by different experts, but they are not represented as rules. But, those tables will be called knowledge bases in this research for convenience.
- 65. Digital Databases Definitionfor 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. Digital Databases Definitionfor 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. Digital Databases Definitionfor 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. Digital Databases Definitionfor Land Suitability Assessment Knowledge bases: Although these are not really knowledge bases in the strict definition, these databases can be called knowledge bases because they contain the information held by the human expert. They will be used for the artificial neural network. These knowledge bases 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. Sys Knowledge base Requirements for 21 crops Tables: User Temperature,Rainfall,Humidity Thematic Databases Solar Radiation,Evaporation Interface Topography,Soils Tables: Ponce Knowledge base Land 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. Land Suitability Assessmentbased on FNN Fuzzy systems and artificial neural networks have similar characteristics and are complementary to each other. Their complementary parallelism properties have led researchers to combine them into the called neuro-fuzzy systems or fuzzy neural networks, which are more suitable for complex and imprecisely defined applications. In fuzzy logic, a linguistic variable like "suitable" can have several linguistic values like "very", "moderate" or "marginal". Each linguistic value is viewed as a fuzzy set associated with a membership function, which can be triangular, bell-shaped, or of any other form (Fu, 1994).
- 71. Land Suitability Assessmentbased on FNN Output layer Output fuzzy layer Conjunction layer Fuzzy set nodes Input layer
- 72. Land Suitability Assessmentbased on FNN Input layer: There are the input values for each one of the three different classes of LC and there values are taken with the user enter all of the parameters of a given LMU to be evaluated. The minimum data should be entered 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 assessed When the user gives the crop name, land characteristic and the value, then the network is activated to start the evaluation. The activation level of each input unit is the value of the LC (Xi) in the given instance, thus, there is not an activation function.
- 73. Land Suitability Assessmentbased on FNN
- 74. Land Suitability Assessmentbased on FNN The input fuzzy layer: In this layer the input data from the previous layer are processed by the summation function, and then the result of this function is evaluated into a membership function (anyone of the 9 membership functions defined in this research). In this layer, in order to use the membership function, some values have to be calculated, these values are from the knowledge bases defined 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 the LUT given, and β is a medium mean between γ and α, it can be calculated by β=(γ+α)/2
- 75. Land Suitability Assessmentbased 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. Land Suitability Assessmentbased on FNN Conjunction layer: the conjunction units take the minimum value of the degrees of the each one of the suitability classes. The activation function in this layer is the min function, this function selects the minimum value from a vector given, It has sense, because on the definition of FAO limitations method, the suitable class will be attributed, to the land characteristics compared with the crop requirements, according to the less favorable land characteristic (Sys, 1985). The conjunction layer says how the neural network takes the decision about which is the beast option for a given input.
- 77. Land Suitability Assessmentbased on FNN θ 3=0 Neuron i,3 in Output Fuzzy Layer S Oi,3 Wi,3=1 n Wi,3=1 Oi,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. Land Suitability Assessmentbased on FNN Output fuzzy layer: The output fuzzy set units collect the information from the conjunction units (each corresponding to a fuzzy rule). The output fuzzy set units use the max function as activation function because the grade of membership of an object based on all fuzzy rules is given for this function. Other important step in the fuzzy systems, the defuzzification, only can be reach by the use of the max function at first, and follows by the centroid method (Fu, 1994).
- 79. Land Suitability Assessmentbased 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=1 W1,4=100 O1,3 O3,3 W3,3=1 n n W2,4=60 ∑O ∑ X W i ,3 i i,2 −θ 2 Suitability Index in O= i =1 i =1 n Suitability class obtained O2,3 ∑O i =1 i ,3 W3,4=40 O3,3
- 80. Land Suitability Assessmentbased on FNN Output layer: The output units generate the final result by integrating the information from the output fuzzy set units. This units calculate its activation level on the defuzzification method, this method refers to translating the membership grades of a fuzzy set into a crisp value, and it is centroid method. The centroid method calculates the crisp value from a given variable given, its calculation finds the centroid/center of gravity of the region bounded by the output membership function of the output fuzzy set units.
- 81. Suitable class 1938 S1 0.6 Membership degree n/N developing stage 0.7731 0.8 n/N maturation stage 50 155.4 0.7731 Base saturation 2 Organic matter kaolinitic 0.7731 0.7731 1.2 27 0.00028 Organic matter non kaolinitic 0.9998 0.7731 0.8 0.0408 Organic matter calcareous 100 0.9591 1938 Depth 850 Annual rainfall 220 0.0555 Length gs 800 155.4 0.00028 0.00028 Rainfall gs 0.405 22 Mean temperature gs 27 16 Mean min. temperature gs 0.7731 30 0.5286 Mean humidity developing stage 0.0408 0.00028 1938 0.00015 2 0.0408 slope type 2 (high level) 0.0408 slope type 2 (low level) 4 99.9 Coarse fragmentation 6 155.4 CaCO3 2 Suitability Index 8 27 99.9 Gypsum
- 82. The approach proposedhere could be translated into a computer software called LANSAS. LANSAS must be capable to carry out intensive land suitability assessments, and allowing users to access to 3 different knowledge bases with data about LUT requirements for a wide variety of crops for the most grown under rainfed agriculture. Additionally, LANSAS will be design as to use geographic information from standard GIS software (raster and vector). The capabilities of LANSAS to import-export data from standard relational databases management systems, are too part of its design. The user interface of LANSAS could be friendly enough to be used by any kind of users. And its knowledge bases resolve the old problem of lack of about to not accessibility of this kind of information.
- 83. The computer systemwill be designed, and developed adopting the prototyping 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. 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 beans Steps 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. 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 production prediction = 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. 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 ij Where: r= repetitions in blocks; G= correction factor represented by G=(Y2)/(r Σ t j=1 rj); Bi=β i; Tj= τ j;
- 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. 75 example of the experimental design applied in the Texcoco river watershed to test level of accuracy prediction between LANSAS and other current paradigms in land suitability assessment.
- 88. Treatments rj rrj Tj Mean Lansas 4 8 408 51 Reality 4 8 406.7 50.8 Sum 8 16 814.7 50.9 Example of results obtained from completely randomized blocks method