=Paper=
{{Paper
|id=Vol-2984/paper5
|storemode=property
|title=Application of decision tables transformations for prototyping knowledge bases in the case of forest fire risk forecasting (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2984/paper5.pdf
|volume=Vol-2984
|authors=Aleksandr Yu. Yurin,Olga A. Nikolaychuk,Nikita O. Dorodnykh
|dblpUrl=https://dblp.org/rec/conf/itams/YurinND21
}}
==Application of decision tables transformations for prototyping knowledge bases in the case of forest fire risk forecasting (short paper)==
https://ceur-ws.org/Vol-2984/paper5.pdf
Application of decision tables transformations for prototyping
knowledge bases in the case of forest fire risk forecasting
Aleksandr Yu. Yurin, Olga A. Nikolaychuk and Nikita O. Dorodnykh
Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of Russian Academy of Sciences
(ISDCT SB RAS), Lermontov St. 134, Irkutsk, Russia
Abstract
In this paper, we consider the application of the PESoT technology and a tool (namely,
Personal Knowledge Base Designer) for prototyping rule-based knowledge bases by using
the automated analysis and transformation of decision tables presented in the CSV format.
Created prototypes of knowledge bases designed for intelligent decision-making support
when analyzing and forecasting the risk (probability) of forest fires based on information
about the forest fire hazard class, weather conditions, and other factors. A description of the
main stages of the approach and an illustrative example are presented.
Keywords 1
Knowledge bases, transformation, decision table, fire risk, forest squares, PESoT, PKBD
1. Introduction
The complexity of creating intelligent systems and their knowledge bases can be reduced with the
use of methods and tools based on the paradigm known as End-User Development (EUD), including
End-User Programming (EUP) and End-User Software Engineering (EUSE) [1-3]. Visual
programming and Model-Driven Development (MDD) are examples of EUD methods that are
implemented within the PESoT technology (Prototyping Expert Systems Based on Transformations)
[4-7]. The main benefit of these EUD methods and technology applications is reducing the risk of
manual coding errors, and reusing conceptual models developed earlier.
One of the tasks that require the use of these methods is the development of an intelligent system
in the form of a thematic WPS service to support forecasting the risk of forest fires. This task is a part
of the grant No. 075-15-2020-787 of the Ministry of Science and Higher Education of the Russian
Federation "Fundamentals, methods and technologies for digital monitoring and forecasting of the
environmental situation on the Baikal natural territory" [8].
Two techniques for forming fire risk evaluations in forest quarters (squares) were considered when
solving this task:
• The first technique is based on a statistical analysis of information about forest fires for the
previous period, taking into account a certain forest quarter and a time interval. So, this technique
involves statistical processing of large amounts of data, and the resulting evaluations do not
depend on the query conditions;
• The second technique is based on artificial intelligence methods, in particular, rule-based
expert systems. This technique involves not only statistical processing of data but also conceptual
modeling, data mining to find patterns and their further formalization in the form of logical rules.
In this case, the obtained evaluations take into account the query conditions.
ITAMS 2021 – Information Technologies: Algorithms, Models, Systems, September 14, 2021, Irkutsk, Russia
EMAIL: iskander@icc.ru (A. 1); nikoly@icc.ru(A. 2); tualatin32@mail.ru (A. 3)
ORCID: 0000-0001-9089-5730 (A. 1); 0000-0002-5186-0073 (A. 2); 0000-0001-7794-4462 (A. 3)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
In this paper, we consider the application of the PESoT technology for the automated creation of
knowledge bases including formalization and code generation tasks in the context of the second
technique. So, the main contribution of the paper is a description of the application of this technology
when solving the task of decision-making support for forest fire risk forecasting.
The paper is organized as follows. Section 2 presents a background, including the main principles
of the PESoT technology, our motivation, and the brief state of the art in the field of forest fire
forecasting. Section 3 contains the application including a detailed description of separate steps, and
an illustrative example, while Section 4 presents some concluding remarks and future works.
2. Background
Next, let’s consider the main principles of the used PESoT technology, as well as the subject
domain (forecasting the risk of forest fires).
2.1. PESoT: Prototyping Expert Systems Based on Transformations
The PESoT technology [4-7] implements the model-driven EUD principle and includes methods
and tools for prototyping rule-based expert systems and decision-making software components for
intelligent systems.
Formally, this technology can be described by the following [4]:
MDE ES = MOF , LES , CIM ES , PIM ES , PSM ES , PDM ES , FCIM
ES ES ES
− to − PIM , FPIM − to − PSM , FPSM − to − CODE
where LES is a set of languages and formalisms used for modeling expert systems and components;
in our case LES = {UML, CM , DT , CT , RVML} where UML is a Unified Modelling Language; CM is a
concept or mind maps formalism; DT is a formalism for the representation of decision tables; CT is a
formalism for the representation of canonicalized tables; RVML is a Rule Visual Modeling Language
[5];
CIMES is a computation-independent model for PESoT, in our case, it is a domain model
represented with the aid of LES;
PIMES is a platform-independent model for PESoT, in our case this model represent logical rules in
our notation RVML;
PSMES is a platform-specific model for PESoT, in our case this model takes into account the
features of the programming language, we use RVML;
PDMES is a set of platform description models for PESoT, in our case
PDM ES = {CLIPS , DROOLS , PHP, PKBD} .
ES
FCIM ES ES
−to − PIM , FPIM −to − PSM , FPSM −to −CODE
are the rules for model transformations.
The process of creating prototypes of knowledge bases and expert systems is represented by the
sequence (a chain) of the following stages: building domain models, building platform-independent
models, building platform-specific models, generating source codes and specifications, testing, and
integration.
A detailed description of the technology is given in [4-6]. Below, a detailed description of the
main stages of its application is considered.
2.2. Motivation
The motivation of this work is due to the grant No. 075-15-2020-787 of the Ministry of Science
and Higher Education of the Russian Federation "Fundamentals, methods and technologies for digital
monitoring and forecasting of the environmental situation on the Baikal natural territory" [8]. This
project includes solving a set of tasks, one of which is the development of thematic WPS services for
digital monitoring, analysis, modeling, and forecasting of the environmental situation, as well as the
risk of natural and technogenic fires.
Researches are being conducted to solve this problem that is linked to:
• Collecting, cleaning, and analyzing forest fire data and identifying specific heuristics. These
heuristics allow one to identify the territorial risk of forest fire hazard that will be considered as
fire risk for forest quarter (aka dacha or forestry);
• Developing a knowledge base for analyzing and forecasting the risk (hazard) of fires based on
information about the forest fire hazard class, weather conditions, and other factors.
2.3. State of art: Forecasting the risk of forest fires
The topic of the automated formation of knowledge bases for intelligence decision-making support
in forecasting the risk (danger) of fires in forest quarters is poorly represented in the scientific
literature. There are some works related to a model transformation and the application of a model-
based approach (e.g., MDD), as well as some papers aimed at forecasting the risk (hazard) of forest
fires.
The first group of works is considered in more detail in [4-6].
Several following works that illustrate the main areas of research that can be selected for the
second group:
• The identification of factors affecting the fire hazard of forests [9-10], including a height, a
slope, a topographic humidity index, a distance from urban areas, an average annual temperature,
land use, a distance from roads, average annual precipitation, a distance to rivers, air temperature
(average daily and maximum), dates of the transition of average daily temperatures through
threshold limits, dates of onset and descent of a stable snow cover, a relative humidity (average
daily and minimum), an air humidity deficit, a count of days with relative humidity < 30% in one
of the observation periods for a certain period, an annual precipitation regime, a count of days with
rain, a dryness index, a wind regime, a count of days with thunderstorms, etc.;
• The improvement of the scale for assessing forest fire hazard classes depending on weather
conditions [11] to account new factors, in particular, humidity indicators [12], or taking into
account regional features [13];
• The use of existing methods for assessing fire risk in different regions [14].
In the context of this study, the above works were used to analyze the subject domain and identify
factors affecting the assessment of forest fire risk.
3. Decision tables transformations for prototyping knowledge bases
The PESoT technology provides the automated formation of knowledge bases by using various
information sources, including conceptual models and tables of different types. One of the tabular
forms supported by this technology is a specialized form of decision tables described below.
3.1. A specialized form of decision tables
The specialized form of decision tables is an extension of the standard one [15] and consists of
columns and rows. Columns represent names of independent and dependent properties (components
or parts of rules), and rows represent specific rules. At the same time, table cells contain values of
their properties. The tabular form used in this work has some features. In particular, these features
were determined by further automated processing of tables in the context of knowledge engineering.
The main features of our tabular form:
• A table may contain a column with rule names; it must be the first and have the "Rule Name"
name;
• Headers of dependent columns are marked with the "#" symbol;
• A column header name can be compound, indicating an entity name (or a class name) and its
property name separated by "::" string.
• There are no restrictions on values in cells, i.e. they can not only consist of a set of values
{yes, no} as, for example, in [16]. So, values in cells can contain specific arbitrary values and not
only values that indicate the presence or absence of certain property (component) in a rule
structure.
A specialized decision table fragment is presented in Figure 1.
Figure 1: An example of a specialized decision table fragment.
This form of decision table provides the generation of logical rules of the "IF-THEN" type. In
particular, an example of a decision table fragment presented in Figure 1 is interpreted as follows: IF
there is a "Risk" of a certain "grade" and "kind", and a «Flood hazard» of a certain "level" and
"probability", THEN some "Conclusion" with a certain "text" and "cf" (a certainty factor) is made.
Accordingly, values for properties "grade", "kind" etc. are taken from the cells of a certain row.
The advantages of this form of representation of source data are the following:
• It is the most popular way to represent logical rules for non-programming end-users;
• It is used to represent the results of data mining, for example, when using the Deductor Studio
or Loginom system (Base Group company);
• It provides the ability to use publicly available and widespread software, such as Microsoft
Excel for generating data, and then saving them in the CSV format;
• There are examples of using this form when solving various practical tasks [17-19].
3.2. Main stages
The development process with the aid of PESoT and the PKBD (Personal Knowledge Base
Designer) tool is similar to other PESoT cases [17] and can be presented in the form of the following
scheme (Figure 2).
Figure 2: Knowledge base development using PKBD.
In the current case, stage 2 has the greatest computational complexity. This stage is associated with
data analysis and the formation of decision tables. Let’s consider the stages of this approach using an
illustrative example in more detail.
3.3. An illustrative example
The task of prototyping knowledge bases for determining the risk of forest fires is considered an
illustrative example. Information on forest fires in the Baikal natural territory for 2017-2020 years,
weather data, as well as information on infrastructure (roads, settlements, etc.) and the type of
vegetation were used as initial data. The database on fires includes more than 45 000 records
describing information about heat points identified as a result of the analysis of satellite images.
Important and computationally complex tasks associated with the preparation of this data for the
development of knowledge bases are the following:
• Grouping (aggregating) information about fires with the definition of duration, minimum and
maximum area of a certain fire;
• Determining fires located within the boundaries of industrial zones, settlements, and mining
zones that are not natural fires;
• Determining fire statistics for certain classes of fire hazard based on forest plans of districts of
the Irkutsk region (taking into account the structure of the forestry, aka plots (dachas) or quarters);
• Determining a set of independent factors influencing the risk of fire hazard in a forest district;
• Calculating factor values affecting the risk of the fire hazard of a forest district and their
transformation to interval or fuzzy form;
• Determining the risk (hazard) of a forest fire based on the current values of a complex of
factors through a certain class of fire hazard and its statistics.
These tasks will be considered in more detail in other works. In this paper, the process of building
knowledge bases based on the analysis of decision tables is considered, and we assume that the data
preprocessing has already been completed.
In this case, the knowledge base consists of two segments and solving the following subtasks:
1. Forming a conclusion on the forest fire risk of a certain forest area based on the average
monthly weather data, current weather conditions, information about the time of year, the
proximity of rivers, lakes, roads, settlements, terrain, and vegetation type;
2. Forming a conclusion on the risk of a forest fire according to the fire hazard class of a certain
forest area using fire statistics and information about the time of year (season).
Figure 3: A fragment of the domain conceptual model.
Next, we will consider the stages in more detail.
Stage 1. As a model of the domain, a conceptual model was created that describes the factors
affecting the class of the fire hazard of a forest area and the risk (probability) of a fire. A fragment of
this model is shown in Figure 3.
Stage 2. Next, decision tables were developed that describe the structural aspect of the domain.
These tables contain information about combinations of features describing the fire hazard class of a
forest area and the risk (hazard) of a forest fire.
In particular, the following table structure (headers) is used to define the hazard class:
Road::distance_to_car_road, Road::distance_to_railway, River::distance_to_river,
Lake::distance_to_lake, Meteodata::rrr, Meteodata::ff, Meteodata::u, Meteodata::t,
Settlement::distance_to_settlement, Settlement::population, Region::population,
Region::average_annual_temperature, Season::name, Forestry::staff_number,
Square::landform, Square::forest_type, Square::underlying_surface_type,
#Square::name, #Square::fire_hazard_class.
To determine the risk (hazard) of a forest fire, the following table structure (headers) is used:
Square::name, Square::fire_hazard_class, Season::name, #Fire::risk[probability].
Further, the obtained rules are analyzed to define the indicators of the frequency of their
appearance in the analyzed data. In particular, the support and confidence of the rules are determined.
The confidence is used as a certainty factor of a rule.
The decision table with intermediate data is shown in Figure 4.
Figure 4: A fragment of the decision table with intermediate data.
Stage 3. Next, with the aid of PKBD [6] (it is a tool of PESoT), the decision tables were imported
and presented in the form of logical rules. The imported decision tables were refined in the RVML
(Rule Visual Modeling Language) form (Figure 5).
Figure 5: Rule templates (generalized rules) for the formation of specific knowledge base rules.
Stage 4. For two segments of the knowledge base, a code was generated on CLIPS, which was
used to debug the obtained knowledge bases, later presented in the form of PHP codes (Figure 6).
Stage 5. Testing and integration will be done in the future.
4. Conclusion and Future Works
In this paper, we consider the use of the PESoT technology and tools for prototyping rule-based
knowledge bases by using automated analysis and transformation of decision tables. The formed
knowledge bases can be used to create an intelligent decision-making support software module in the
form of a WPS service for analyzing and forecasting the risk (hazard) of forest fires based on
information about the forest fire hazard class, weather conditions, and other factors. An illustrative
example demonstrating the fundamental applicability of this approach is presented.
Figure 6: Fragments of generated codes.
The technology is designed for end-users and reduces the time for creating prototypes of AI
modules and expert systems by automating the codification stage and using existing domain models.
Our approach has a certain level of universality and after its improvement can be used in various
domains, for example, for solving tasks in the field of industrial safety inspection [20].
In the future, we plan to make a quantitative evaluation of the proposed technology by conducting
computational experiments.
5. Acknowledgements
The present study was supported by the Ministry of Education and Science of the Russian
Federation (Project no. 121030500071-2 "Methods and technologies of a cloud-based service-oriented
platform for collecting, storing, and processing large volumes of multi-format interdisciplinary data
and knowledge based upon the use of artificial intelligence, model-driven approach, and machine
learning"). Results are achieved using the Centre of collective usage «Integrated information network
of Irkutsk scientific educational complex».
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