Analysis of Groundwater Quality using Mamdani Fuzzy Inference System (MFIS) in Yazd province, Iran

Saberinasr, Amir; Rezaei, Mohammad; Barmaki, Majid Dashti

doi:10.5120/9564-4033

Cited by 16 publications

(8 citation statements)

References 44 publications

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“…The fuzzifier maps input variables: knowledge, forum_participation, and attendance, in a fuzzy set with a membership function approach. The fuzzy input value from the fuzzifier with the fuzzy rule base is processed by the fuzzy inference engine to become the fuzzy output value [15] [16]. Defuzzifier converts the fuzzy output value into a crisp value as a student performance score.…”

Section: 4mentioning

confidence: 99%

“…where is the number of rules, is the rule number, and are the fuzzy sets, is the antecedent variable representing the input in the fuzzy system, and is the consequent variable related to the output of the fuzzy system [16].…”

Section: 2mentioning

confidence: 99%

“…The inference system handles how the rules are combined. The Max-Min and Max-Product methods are the most commonly used techniques for the composition of fuzzy relations [16]. Equation 3shows the Max-Min composition used in the fuzzy inference system.…”

Section: 3mentioning

confidence: 99%

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Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

Wardoyo

Yuniarti

2020

IJID

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The phenomenon of the rapid transfer of learning to online systems, such as e-Learning, has occurred massively. Institutions must ensure that student assessments run well. The characteristics of learning in e-Learning require an appropriate assessment method. The fuzzy logic method can be an option. Research shows that fuzzy logic is capable of providing flexible and objective performance evaluation. Fuzzy logic is a method that can overcome the uncertainty of transparency and objectivity of student assessments. In general, fuzzy logic applications are carried out by standards. Modification is an attempt to reveal the flexibility and to optimize the use of fuzzy logic. This study presents an analysis of fuzzy logic modification for the assessment of Algorithm and Data Structures courses held in e-Learning. These modifications include (i) modification of the parameter score with score compatibility, (ii) consequent modification of the fuzzy rules and (iii) modification of the implication process. The study results show that although the use of fuzzy logic requires more complicated procedures and tools, it can present various kinds of assessment as an option for educators to assess students in e-Learning.

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Section: 4mentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

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Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

Wardoyo

Yuniarti

2020

IJID

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“…Quality of groundwater could analyze by A. Saberi Nasr et al in Yazd Province, Iran. The researchers proposed mamdani fuzzy inference system to analyze the groundwater into acceptable and non-acceptable group [17]. Keunje Yoo et al analyzed hydrological parameters and groundwater pollution with the use of decision tree based technique.…”

Section: Review Of Literaturementioning

confidence: 99%

Preprocessing the Groundwater Quality data by LSR and QDR techniques

Rajamani*,

Thambusamy

2019

IJITEE

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Data mining is the process of identifying patterns and their relationships to solve problems through data analysis. Data mining is utilized to haul out working information from a colossal dataset of any crude information. Environmental mining is one of the wide areas to find impact on environment. Data mining encourages the usage of essential strategies and finds noteworthy information from gigantic measure of environmental information. Data preprocessing techniques are very essential in data mining, which uses various techniques to convert the raw data into a meaningful data to further research work. In this research work, Logical Similarity Replacement (LSR) and Quantity based Discrepancy Replacement (QDR) algorithms are proposed to ascertain the quality of groundwater. The numerical information are preprocessed by the statistical techniques Mean, Median methods and non-numeric information are preprocessed by the proposed LSR and QDR methods to satisfy the fragmented and conflicting information in the dataset. The conflicting and the missing information are corrected by the picked strategies for preprocessing. In the wake of applying these preprocessing systems connected in the dataset, the nature of the informational index is improved.

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“…However, due to existing large number of obscure parameters with SLL prediction, the theoretical governing equations may not be of much advantage for this purpose. Over the past years, Artificial intelligence techniques such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) have been widely used to predict different non-linear parameters in the field of hydrology such as rainfall-runoff, ground water quality and sediment loads in rivers and the results demonstrated their efficiency [3][4][5]. Recently, a conjunction model of wavelet and artificial intelligence techniques has been applied successfully to predict SSL in rivers in which were superior to previous techniques [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Wavelet-ANFIS Model to Estimate Sedimentation in Dam Reservoir

JavadAlizdeh¹,

Joneyd²,

Motahhari³

et al. 2015

IJCA

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An accurate prediction of sedimentation in dam reservoir is a challenging issue due to the complex and non-linear physics of the problem. Anyhow, soft-computing-based techniques showed great ability for predicting non-linear phenomena and have been used for different purposes. The main objective of this study is to estimate the volume of sedimentation in Karaj dam using a wavelet-ANFIS (WANFIS) and a wavelet-neural network (WANN) model. Monthly average flow is used to estimate monthly averaged suspended sediment load for a thirty-year period. The amount of bed load is computed based on the suspended sediment load and the river slope and the total volume of sedimentation in the reservoir is calculated with subtracting the upstream (Karaj River) and downstream (Beylaghan River) total sediment load. In WANFIS and WANN models, monthly average flow time series are decomposed to several sub-time series using different wavelet decomposition levels. The total volume of sedimentation in Karaj dam obtained from different techniques such as WANFIS, WANN, ANFIS, ANN and hydrography are compared together. The comparison demonstrates that WANFIS model is superior to the other techniques. For WANFIS and WANN models, the best model is obtained by two and three wavelet decomposition levels respectively. Findings of this study reveal that Wavelet-ANFIS models can be applied as a successful tool to predict the volume of sedimentation.

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Analysis of Groundwater Quality using Mamdani Fuzzy Inference System (MFIS) in Yazd province, Iran

Cited by 16 publications

References 44 publications

Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

Preprocessing the Groundwater Quality data by LSR and QDR techniques

A Wavelet-ANFIS Model to Estimate Sedimentation in Dam Reservoir

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