Desarrollo del sistema LANSAS

Trent University
Watershed Ecosystem Graduated Program

A COMPUTER SYSTEM
FOR
LAND SUITABILITY ASSESSMENT
BASED ON FUZZY NEURAL NETWORKS

Research Proposal

A COMPUTER SYSTEM
FOR

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

A COMPUTER SYSTEM
FOR

INTRODUCTION

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Land Suitability Assessment is 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


LCh
Climate
Soil
Socio-economic

LMU


LCh
Climate
Soil
Socio-economic

LUR
Climate
LUT
Soil
Socio-economic

LMU


LCh
Climate
Soil
Socio-economic

Matching
LUR
Climate
LUT
Soil
Socio-economic

LMU


LCh
Climate
Soil LUT Suitable
Socio-economic
Or
NOT Suitable
on LMU
Matching
LUR
Climate
LUT
Soil
Socio-economic performance

LMU

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.

Yield
Suitability Classification

Very suitable

Moderately suitable

Marginally suitable

Non suitable
0
LCh performance on LUT


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.

Yield

Very suitable

Moderately suitable

Marginally suitable

Non suitable
0
LCh performance on LUT

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]

A COMPUTER SYSTEM
FOR

PROBLEMS IN CURRENT APPROACHES

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Current approaches have a 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.

LUR data are not available for all kinds of individuals involved in land
evaluation

The knowledge base used is site-specific

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

evaluation.


Accuracy varies with the state of knowledge

LUT performance
• Current approaches are based on N S3 S2 S1

"crisp" classification systems

LCh values

evaluation.


Accuracy varies with the state of knowledge.

Current approaches are based on "crisp" classification systems

•Current computer systems lack a built-
in knowledge base

A COMPUTER SYSTEM
FOR

PROPOSAL OF SOLUTION,
HYPOTHESIS
AND
RESEARCH OBJECTIVES

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Research Proposal

This research advances 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.

Hypothesis

The research in this 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

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

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.

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.

Objectives

This research project sets 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.

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.

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.

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.

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.

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FOR

Research progress
And
Findings

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LUR data and Knowledge 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)

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.

Applications of fuzzy set 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

However, Huajun's algorithm and 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

LUR or LCh do 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

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

Suitability membership functions changes 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


1 LUT Performance

Degree Class 3
Of
membership


Land suitability assessment based 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

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

Land suitability assessment system

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.

The databases and files 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

LANSAS has been designed 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

The capabilities of LANSAS 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

LANSAS knowledge bases resolve 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

The Graphic User Interface (GUI) of LANSAS is friendly
enough to be used by any kind of users.

A COMPUTER SYSTEM
FOR

Current state of this research and results

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•The field research and 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.

•LANSAS user interface is 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.

•An evaluation of the 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.

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FOR

Conclusion

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Conclusion

It is expected that 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

LAND SUITABILITY APPROACHES

DIGITAL KNOWLEDGE BASES

DIGITAL DATABASES

FUZZY NEURAL NETWORK FOR LSA

LANSAS CICLE OF LIFE

TEST AND VALIDATION

Limitation Method

The limitation methods 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.

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

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

Parametric Method
This approach consists 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

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

Decision Trees Method

One of 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.

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

Digital Knowledge Bases

At present, 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.

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

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.


A 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 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.

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

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

•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


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 )

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)
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
Tables:
Climatic zones,Temperature,
Evaluation Rainfall,Growing period, Light,
Day-length,Soils
Modules
Soil Climatological
Classification classification

Land Suitability Assessment based 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).


Output layer

Output fuzzy layer

Conjunction layer

Fuzzy set nodes

Input layer

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.


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

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


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.

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


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).

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


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.

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

The approach proposed here 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.

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

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)

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

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;

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.

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

Desarrollo del sistema LANSAS

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Desarrollo del sistema LANSAS