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DETERMINATION OF WATER QUALITY INDEX USING FUZZY LOGIC

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Archana Raju

on 6 September 2016

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Transcript of DETERMINATION OF WATER QUALITY INDEX USING FUZZY LOGIC

DETERMINATION OF WATER QUALITY INDEX USING FUZZY LOGIC
Project Guide : Dr S Amalraj
Archana Raju
Kiran Kaushal
Thayn Malar

FUZZY LOGIC FOR DETERMINING THE WATER QUALITY OF LAKES
SAMPLING AND WATER QUALITY ANALYSIS
ANALYSIS OF WATER QUALITY PARAMETERS
LABORATORY TESTING METHODS
CONTRIBUTION OF EACH PARAMETER TO THE FUZZY INDEX VALUE
LITERATURE REVIEW
WATER QUALITY INDEX
In the mid-twentieth century water quality was categorized by Horton in 1965.
Then in 1970 Brown et al. developed a general Water quality index (WQI).
In 1982 Steinhart et al. applied a novel environmental quality index to sum up technical information on the status and trends in Great Lakes ecosystem
In the mid-1990s WQI was introduced in Canada by the water quality guidelines task group of CCME.

FUZZY LOGIC
Fuzzy logic was introduced by Zadeh (1965)
Great attention has been paid to the development of environmental indices based on fuzzy logic (Gharibi et al., 2012; Lu et al., 1999; Mirabbasi et al., 2008; Sasikumar and Mujumdar, 1998;Sowlat et al., 2011)
The fuzzy water quality index (FWQ) developed by Ocampo-Duque et al. (2006) included twenty seven parameters
MATERIALS AND METHODS
FUZZY LOGIC
Fuzzy Logic is a new approach to computing based on degrees of truth, differing from the true or false Boolean logic of the modern computer.
Fuzzy logic includes 0 and 1 as extreme cases of truth but also includes the various states of truth in between.
Fuzzy logic appears similar to the reasoning of the human mind
The fuzzy system is capable of working with complex systems under uncertain and imprecise conditions.
COMPONENTS OF FUZZY INFERENCE SYSTEM
Membership functions
: curve that defines how each point in the input space is mapped
Fuzzy set operators
: defines relationships among the fuzzy subsets
Inference rules
: relates input conditions to output responses
ARCHITECTURE OF FIS SYSTEM
BASIC METHODOLOGY
Sample collection and laboratory analysis
Parameters are assigned range, membership functions and linguistic terms
Inference rules are created based on expert knowledge
Aggregation : fuzzy sets that represent the outputs of each rule are combined into a single fuzzy set
Defuzzification : conversion of fuzzy output to a crisp value
Validation and comparative analysis
The samples collected from three different sampling stations earmarked as R1, R2, R3 for Rajakilpakkam while V1, V2, V3 for Vengaivasal lakes respectively.

The surface water collected from the six locations were grab samples in 2L polyethylene bottles for a period of two months from February 2016 to March 2016 (Post -Monsoon).
GOOGLE MAP SHOWING THE LOCATION OF LAKES
SATELLITE VIEW OF SAMPLING STATIONS AT RAJAKILPAKKAM LAKE
Following the sample collection, the samples were labelled and preserved in the refrigerator.
Samples were tested according to APHA 2012 standards.

SAMPLES COLLECTED FROM RAJAKILPAKKAM AND VENGAIVASAL LAKES
MEAN WATER QUALITY PARAMETERS
SAMPLE COLLECTED BY GRAB SAMPLES
Fuzzy index was developed using Fuzzy logic tool box of Matlab (R2015a).
In fuzzy index development, ten quality indices were included based on their importance .
The ten parameters were grouped into five sets which was fed to inference engine

FUZZIFICATION AND MEMBERSHIP FUNCTIONS
Fuzzification involves two processes: derive the membership functions for input and output variables and represent them with linguistic variables.
Two kinds of membership functions were used in the present study: triangular and trapezoidal
Based on the expert opinion , the ranges and priority were assigned
FUZZY LOGIC TOOL BOX
FUZZY SETS AND LINGUISTIC TERMS
FUZZY CONTROL RULES
Fuzzy control rule can be considered as the knowledge of an expert in any related field of application
The IF-THEN rules set of Fuzzy logic tool box was used in the creation of the model, a total of 125 rules were created
Some examples are :
i. If DO is good and BOD is very poor, fuzzy index is very poor.
ii. If pH is excellent and Temperature is good, fuzzy index is good

DEFINING RULES FOR FUZZY SYSTEM
DEFUZZIFICATION
Defuzzification of the outputs for the fuzzy inference system were carried out using the Centre of Gravity method (COG).
The COG method, compared to other methods of defuzzification is the most conventionally and physically applicable method
SURFACE GRAPH
OUTPUT RULE VIEW
FUZZY INDEX OUTPUTS
FUZZY INDEX VALIDATION
One of the most critical approaches is the methodology used in developing the index, that is, fuzzy logic
The second approach addresses the inclusion of the parameters in the index.
In the third approach, we assessed the robustness of the fuzzy index a sensitivity analysis
The fourth method involves validation by comparing the fuzzy index with the conventional WQI created.
SENSITIVITY ANALYSIS
CONVENTIONAL WQI
SATELLITE VIEW OF THE SAMPLING STATIONS AT VENGAIVASAL
STUDY AREA
(a) Rajakilpakkam
(b) Vengaivasal
CREATION OF FUZZY INFERENCE SYSTEM
We considered the final rule set of the index as the baseline, and then measured the changes or deviations, which were intentionally made
This can be illustrated by a nearly 80% change in the fuzzy rules of the first stage which changed the index outputs by only 8%, implying that the index is quite robust against the changes.
The fuzzy index is validated by comparing the fuzzy index with the conventional Water Quality Index created by the U.S National Sanitation Foundation.
WQI is defined as a rating that reflects the composite influence of different water quality parameters (Sahu and Sikdar 2008).
STEPS FOR WQI CALCULATION
COMPARISON OF FUZZY INDEX WITH WQI
In all the sampling stations, the fuzzy index shows a maximum deviation of less than 4% of the actual WQI, hence the proposed index produces stringent results and is robust to changes based on the analysis.
COMPARISON OF FUZZY INDEX WITH PAST WATER QUALITY DATA
A comprehensive analysis of the accuracy of our proposed Fuzzy-based index was done based on the overall WQI developed for the lakes in the year 2010.
MEMBERSHIP FUNCTIONS
Each of the chemical parameters was assigned a weight
The relative weight of each parameter was computed
A quality rating scale (qi) for each parameter was computed
Finally, for computing the WQI, the water quality sub-index (SIi) for each chemical parameter first determined, which was then used to determine the WQI
We can understand the effect of each parameter on the fuzzy index, based on the minimum and maximum values taken for each parameter from their respective ranges.
CONCLUSION
Based on the findings, it is conclusive that the water from these lakes is unfit for human consumption

The fuzzy-based water quality index we developed produced more stringent results than those of the WQI due to the distinct computational method
It is a comprehensive tool for assessment of water quality, especially when assessing water for human consumption.
RAJAKILPAKKAM
Located in the south peri-urban fringe of the city.
The length, depth and the area of the lake are 1350m, 2.3m and 2.46sq.km
Surrounded by high density and commercial complexes

VENGAIVASAL
Located 25km from the city limits
The length, depth and the area of the lake are 2020m, 3.25m and 4.61sq.km.
Surrounded by sparse residential area on the west and crop fields in the east.

SCHEMATIC FLOW CHART
DISCUSSION OF LAKE WATER QUALITY
The peri-urban Rajakilpakkam lakes has an overall water quality index of 32.2 (Poor)whereas Vengaivasal lakes has a fuzzy index value of 56.1
In the peri-urban lake, pH varied between (6.2-7.5), the results of pH for Vengaisal did not show much difference
The average temperature of the present study ranged from 27.77 to 28.33 ˚C and 29.23 – 30.57 for Rajakilpakkam and Vengaivasal respectively
Mean DO of Vengaivasal lakes is higher than Rajakilpakkam Lake by 0.04mg/L
In Rajakilpakkam the, mean TDS and EC values were higher (503.34 mg/L, 839.33µS/cm) compared to (356.5 mg/L, 597.17µs/com) in Vengaivasal.
The monthly values for biochemical oxygen demand (BOD3), was a little higher in the Vengaivasal Lake
To develop a novel fuzzy index for water quality assessment
The parameters included in the WQI are taken as a basis and other critical quality parameters are added
To represent the water quality of two lakes namely Rajakilpakkam and Vengaivasal.

NEED FOR STUDY
INTRODUCTION
Water is the prime natural resource.
In this modern era there is a huge decline in freshwater
The water quality evaluation may be complicated practice
So, water quality indices are such approaches which minimize the data volume to a great extent and simplify the expression of water quality status.

CONVENTIONAL WQI TABULATION
THANK YOU
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