Now we define the fuzzy inference rules (Rule Block -1 –RB1) to the first four parameters mentioned in the table no -1 as inputs with the physical quality of the water as output in the following way using Mamdani method Table -2 Output result from the application of IF –THEN rules,with membership function. Now we define the fuzzy inference rules (Rule Block -2 –RB2) to the next five parameters mentioned in the table no -1 as inputs with the chemical quality of the water as output in the following way using Mamdani method Table -3 Analyses of water quality output values Now we define the fuzzy inference rules (Rule Block -3 –RB3) to the parameters, physical and chemical as inputs with the complete quality of the water as output in the following way using Mamdani method Table -4 Analyses of water quality output values STEP: 4 Defuzzification : Defuzzification is processes to get a non fuzzy control action that best represent the possibility distribution of an inferred fuzzy control action [12]. CENTRE OF AREA METHOD The widely used COA strategy generates the centre of gravity of the possibility distribution of a fuzzy set C .The method gives . Figure-11 Graph of water quality output values In this above figure the physical parameter as input values.It shows the out put result. Figure-12 Graph of water quality output values In this above figure the chemical parameter as input values.It shows the out put result Figure-13 Graph of water quality output values 7.0 RESULT AND
We did 3 different test to help conclude the water quality. The first testing/station i did was to see what kind of critters were living in the water. Then, my next group was to take a test to see if eutrophication was in the water. We also took
The first station we had was counting crustaceans and macroinvertebrates in the water. We found 113 critters that belonged in group 1 which means it is quality water. There were 2 organisms in group 2 which was somewhat quality water. Also, 16 critters in group 3 which means the water is very polluted. Overall, based on the critter
In order to evaluate the efficiency of the proposed method over original Apriori and FApriori [9], experiment has been conducted several times and compared the results in 3 ways.
Apart from the single objective functions considered for this problem, a combined function is also used to perform the multi-objective optimization for the FMS parameters. The function and the variable limits are given using following function. Equal weights are considered for all the responses in this multi-objective optimization problem. Hence W1 and W2 are equal to 0.5.
We have performed three test to investigate the concentration of metal, pH of water, water temperature and the level of dissolved oxygen in the river. It helps us to discover the reason of the event and find out solution.
pH of (7.68, 7.95) for the sample and its duplicate and they were within the normal ranges of the surface water pH (6.5-9.0). Conductivity of (178, 186) µS/cm for the sample and its duplicate and they were within the normal ranges of (150 to 500 µS/cm) that support diverse aquatic life. TDS of (106, 111) PPM for the sample and its duplicate and they are within the normal ranges ˂ 500 PPM, “in areas of especially hard water or high salinity, TDS values may be as high as 500 mg/L”. Temperature of (51.8, 55.5) F° for the sample and its duplicate and they were within the normal ranges ˂ 77 F°. Nitrate of (0.2, 0.8) mg/l for the sample and it duplicate and they were within normal ranges of the surface water Nitrate ˂ 1 mg/l.
A linear formula idea will be used and the decision variables will be labeled as follow:
The results for DO include 12/3/12: 3.2 Mg/L, 13/3/13 29 Mg/L, 14/3/14 66.73 Mg/L or % saturation? 15/3/15 92% saturation, 15/9/15 85% saturation. I think 66.73 result was % saturation so that is the result I am using. Recordings of Freshwater Creek on the 22nd of February 2016 show results of DO in both Mg/L and % saturation, these results are 6.32 Mg/L and 80% saturation and for air temp 27.13 Degrees Celsius. Resource 4 shows that water and air temperature coincide with the amount of average/normal Mg/L of DO and Degrees Celsius. For 27.13 Degrees Celsius the Mg/L should be around 8, since the result is 6.32 Mg/L this is under normal amount, however it is not at 0 so the water isn’t completely contaminated/polluted. The year 2012 DO was at an extremely low 3.2 Mg/L this could have been a migration of fish such as tilapia which may have caused many fish to die off, however the next year the Mg/L was at 29 which may have been a direct result of either migrant fish killing off other fish or flooding which killed fish. A significant trend to result was the temperature were the temperature only fluctuated by less than 1.5 degree (resource 5) however that trend is broken due to 2016 results of 27.13 Degrees which is an unusual result due to the
It is very important for that water quality is monitored in water supplies and natural aquatic systems
In this project, parameters such as turbidity, COD, BOD, TSS and pH were measured where turbidity was measured according to the turbidity meter (2100N, HACH, USA) procedure meanwhile pH was measured according to pH meter (Crison pH 25) procedure. 3.8.1
In order to assess the water quality of Farm Pond the class collected a variety of data. The class
The aquatic group carried out multiple environmental parameter tests by which two sample of water were taken, at the mouth and body of the Levera Pond, as well as along the seashore of Levera
Following that, the expected values for decision nodes 6 and 7 should also be calculated. The following results were obtained:
Water Quality & Contamination Abstract My report was based on how ground water may be affected by many containments present in our surroundings. I believed that all of the water samples would have been contaminated once mixed with the soil. As I predicted vinegar filtered through the soil and came out fairly clean.
Besides these two methods, there are many other methods or hybrid methods derived from them. It has been proven that the AI based methods are more capable and much confidence of finding the global optimum than traditional methods. However, they usually require extensive numbers (population size times generation size) of individuals evaluation that are computationally time-consuming and are not suitable for real-time OPF application. Many efforts have been made in order to reduce the computation time of using AI based method. In [12], the authors used an improved power flow model which combined with GA to accelerate the overall computation time. However, this method can only show the efficiency for the particular case which is steady state OPF without considering transient stability. [13] and [14] used a new AI based method called Jaya Algorithm and Symbiotic Organisms Search Algorithm, respectively, that shown better convergence than other methods from literatures, as a consequence, the computation time is reduced. To be summarized, these methods tried to improve the convergence rate to improve the computation efficiency, however, the numbers of call of power flow are