Three-Stage Fuzzy Rule-Based Model for Earthquake Non-Engineered Building House Damage Hazard Determination

Indonesia is a country with a high earthquake intensity which brings significant impact on a lot of infrastructure damage, including building houses in every incident of a natural earthquake. The assessment model on earthquake damage with a fuzzy system has previously developed. It was aimed to assess the building damage rate after earthquake events, and it has a particular weakness on both the criteria used and the rate of model accuracy. The study was conducted to develop fuzzy inference model to determine the building damage hazard, especially for non-engineered building houses on a particular earthquake event (mitigation). The model was is a three-stage fuzzy rule-based model using a thousand data of building houses damaged as result of the impact of earthquake in Bener Meriah district, Aceh Province, Indonesia in the 2013 event, the peak ground acceleration (PGA) data, slope data extracted from 30 meters digital elevation model (DEM) and distance from major fault that was extracted from geological structure map. The main contribution of the research that has been done is to develop the function and fuzzy membership for each determinant variable of building house damage hazard and three stage fuzzy inference process to determine the building house damage hazard as an impact of an earthquake event. Using four hundred data of building houses damage as an impact of the earthquake at the same location, a three-stage fuzzy rule-based model that has been implemented in the study was proven to be able to determine the level of damaged building houses especially for non-engineered building houses better than the previous models with model performance by 93%.

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Transfer Learning for Improving Seismic Building Damage Assessment

Remote Sensing ◽

10.3390/rs14010201 ◽

2022 ◽

Vol 14 (1) ◽

pp. 201

Author(s):

Qigen Lin ◽

Tianyu Ci ◽

Leibin Wang ◽

Sanjit Kumar Mondal ◽

Huaxiang Yin ◽

...

Keyword(s):

Damage Assessment ◽

Historical Data ◽

Data Transfer ◽

Rapid Assessment ◽

Model Performance ◽

Building Damage ◽

Assessment Model ◽

Training Data ◽

Superior Performance ◽

The Impact

The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly earthquake-stricken areas rapidly enough. At the same time, the historical samples inevitably differ from the new earthquake-affected areas due to the discrepancy of regional building characteristics. For this purpose, this study proposes a data transfer algorithm for evaluating the impact of a single historical training sample on the model performance. Then, beneficial samples are selected to transfer knowledge from the historical data for facilitating the calibration of the new model. Four models are designed with two earthquake damage building datasets and the performance of the models is compared and evaluated. The results show that the data transfer algorithm proposed in this work improves the reliability of the building damage assessment model significantly by filtering samples from the historical data that are suitable for the new task. The performance of the model built based on the data transfer method on the test set of new earthquakes task is approximately 8% higher in overall accuracy compared with the model trained directly with the new earthquake samples when the training data for the new task is only 10% of the historical data and is operating under the objective of four classes of building damage. The proposed data transfer algorithm has effectively enhanced the precision of the seismic building damage assessment in a data-limited context. Thus, it could be applicable to the building damage assessment of new disasters.

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Development of Computational Assessment Model of Fuzzy Rule Based Evaluation of Groundwater Quality Index: Comparison and Analysis with Conventional Index

SSRN Electronic Journal ◽

10.2139/ssrn.3356282 ◽

2019 ◽

Author(s):

Utkarsh Nigam ◽

Yadav S.M.

Keyword(s):

Groundwater Quality ◽

Quality Index ◽

Fuzzy Rule ◽

Assessment Model ◽

Rule Based ◽

Groundwater Quality Index ◽

Comparison And Analysis ◽

Index Comparison

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Approximate reasoning with fuzzy rule interpolation: background and recent advances

Artificial Intelligence Review ◽

10.1007/s10462-021-10005-3 ◽

2021 ◽

Author(s):

Fangyi Li ◽

Changjing Shang ◽

Ying Li ◽

Jing Yang ◽

Qiang Shen

Keyword(s):

Fuzzy Inference ◽

Fuzzy Rule ◽

Approximate Reasoning ◽

Rule Based ◽

Reasoning Systems ◽

Significant Interest ◽

Recent Developments ◽

Rule Bases ◽

The Given ◽

Computing Approach

AbstractApproximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.

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Fuzzy-Rule Based Adaptive Data Warehouse

International Journal of Applied Evolutionary Computation ◽

10.4018/jaec.2012010103 ◽

2012 ◽

Vol 3 (1) ◽

pp. 47-65 ◽

Cited By ~ 2

Author(s):

Rajdev Tiwari ◽

Anubhav Tiwari ◽

Manu Pratap Singh

Keyword(s):

Decision Making ◽

Fuzzy Inference ◽

Knowledge Workers ◽

Fuzzy Rule ◽

Data Warehouses ◽

Large Reservoir ◽

Inference System ◽

Rule Based ◽

Neuro Fuzzy ◽

New Generation

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

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Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v22n6p412-417 ◽

2018 ◽

Vol 22 (6) ◽

pp. 412-417 ◽

Cited By ~ 4

Author(s):

Patrícia F. P. Ferraz ◽

Tadayuki Yanagi Junior ◽

Yamid F. Hernandez-Julio ◽

Gabriel A. e S. Ferraz ◽

Maria A. J. G. Silva ◽

...

Keyword(s):

Respiratory Rate ◽

Fuzzy Inference System ◽

Prediction Accuracy ◽

Fuzzy Inference ◽

Fuzzy Rule ◽

System Model ◽

Inference System ◽

Rule Based ◽

Rule Based System ◽

Genetic Fuzzy System

ABSTRACT The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.

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Introduction to Fuzzy Data Mining Methods

Handbook of Research on Fuzzy Information Processing in Databases ◽

10.4018/978-1-59904-853-6.ch003 ◽

2011 ◽

pp. 55-95 ◽

Cited By ~ 12

Author(s):

Balazs Feil ◽

Janos Abonyi

Keyword(s):

Data Mining ◽

Model Performance ◽

Fuzzy Rule ◽

Classification Problem ◽

Data Sets ◽

Rule Based ◽

Data Mining Algorithms ◽

Rule Bases ◽

Mining Algorithms ◽

Extraction Model

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

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Fuzzy Rule Interpolation

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch108 ◽

2011 ◽

pp. 728-733 ◽

Cited By ~ 2

Author(s):

Szilveszter Kovács

Keyword(s):

Fuzzy Inference ◽

Fuzzy Rule ◽

Fuzzy Reasoning ◽

Fuzzy Relation ◽

Rule Base ◽

Rule Based ◽

Fuzzy Rule Base ◽

Current Observation ◽

Rule Bases ◽

Takagi Sugeno

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.

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THE USE OF FUZZY RULE-BASED SYSTEMS IN DETERMINING HORIZONTAL PCO PARAMETERS OF GNSS ANTENNAS

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702018000300024 ◽

2018 ◽

Vol 24 (3) ◽

pp. 367-382

Author(s):

Nassau de Nogueira Nardez ◽

Cláudia Pereira Krueger ◽

Rosana Sueli da Motta Jafelice ◽

Marcio Augusto Reolon Schmidt

Keyword(s):

Fuzzy Inference ◽

Fuzzy Rule ◽

Training Data ◽

Data Set ◽

Rule Based ◽

The North ◽

The Difference ◽

Inference Systems ◽

Antenna Calibration ◽

Rule Based Systems

Abstract Knowledge concerning Phase Center Offset (PCO) is an important aspect in the calibration of GNSS antennas and has a direct influence on the quality of high precision positioning. Studies show that there is a correlation between meteorological variables when determining the north (N), east (E) and vertical Up (H) components of PCO. This article presents results for the application of Fuzzy Rule-Based Systems (FRBS) for determining the position of these components. The function Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was used to generate FRBS, with the PCO components as output variables. As input data, the environmental variables such as temperature, relative humidity and precipitation were used; along with variables obtained from the antenna calibration process such as Positional Dilution of Precision and the multipath effect. An FRBS was constructed for each planimetric N and E components from the carriers L1 and L2, using a training data set by means of ANFIS. Once the FRBS were defined, the verification data set was applied, the components obtained by the FRBS and Antenna Calibration Base at the Federal University of Paraná were compared. For planimetric components, the difference was less than 1.00 mm, which shows the applicability of the method for horizontal components.

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Permeability prediction in shaly formations: The fuzzy modeling approach

Geophysics ◽

10.1190/1.1484526 ◽

2002 ◽

Vol 67 (3) ◽

pp. 817-829 ◽

Cited By ~ 6

Author(s):

Jose Finol ◽

Xu‐Dong D. Jing

Keyword(s):

Clustering Algorithm ◽

Fuzzy Inference ◽

Fuzzy Rule ◽

Learning Task ◽

Well Log ◽

Rule Based ◽

Maracaibo Basin ◽

Core Permeability ◽

Rule Based Approach

This paper shows how fuzzy rule‐based systems help predict permeability in sedimentary rocks using well‐log responses. The fuzzy rule‐based approach represents a global nonlinear relationship between permeability and a set of input log responses as a smooth concatenation of a finite family of flexible local submodels. The fuzzy inference rules expressing the local input‐output relationships are obtained automatically from a set of observed measurements using a fuzzy clustering algorithm. This approach simplifies the process of constructing fuzzy systems without much computation effort. The benefits of the methodology are demonstrated with a case study in the Lake Maracaibo basin, Venezuela. Special core analyses from three early development wells provide the data for the learning task. Core permeability and well‐log data from a fourth well provide the basis for model validation. Numerical simulation results show that the fuzzy system is an improvement over conventional empirical methods in terms of predictive capability.

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Algorithm Apriori Association Rule in Determination of Fuzzy Rule Based on Comparison of Fuzzy Inference System (FIS) Mamdani Method and Sugeno Method

Business Management and Strategy ◽

10.5296/bms.v7i1.9410 ◽

2016 ◽

Vol 7 (1) ◽

pp. 103

Author(s):

Muhammad Fadli Arif ◽

Bima Anoraga ◽

Samingun Handoyo ◽

Harisaweni Nasir

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Expert Knowledge ◽

Fuzzy Rule ◽

Percentage Error ◽

Inference System ◽

Rule Based ◽

Accuracy Measurement ◽

First Order

The economic stability of a country can be determined from the changes in the rate of inflation. Inflation is measured by the annual percentage change in the Consumer Price Index. Since there exists some uncertainties in the inflation data, fuzzy logic is one of the ways to analyse the data. Decisions in fuzzy logic can be made using the fuzzy rule-based inference system. Fuzzy rule-based inference can be obtained from expert knowledge, but the knowledge from the experts on the working of a system is not always available. Therefore, the use of association rules approach could solve the problem. Using three methods of fuzzy inferences; namely the Mamdani Methods, zero-order Sugeno method, and the first-order Sugeno method, this study was carried out to determine which method fits to predict the general monthly inflation data in Indonesia. The Inflation data were derived from the inflation of foodstuff price, X1; inflation of food, drinks, cigarettes and tobacco prices, X2; inflation of housing, water, electricity, gas, and fuel prices, X3; inflation of clothing price, X4; inflation of health care price, X5; inflation of education, recreation, and sports prices, X6; and inflation of transportation, communication, and financial services prices, X7. The performance of the three methods was compared using mean squared error (MSE) and mean absolute percentage error (MAPE) as the accuracy measurement to establish the best fuzzy inference method that fits the inflation value. It was found that the most appropriate method which generated the most accurate results to fit the fuzzy inference system to the inflation data was the first-order Sugeno method.

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