Case Study Of Genetic Algorithm
St = Storage at the beginning of the period t.
Qt = Inflow during the period t.
Rt = Release for irrigation in period t.
Ao = Reservoir water surface area corresponding to the dead storage volume.
Et = Evaporation rate for the period t.
C. Reservoir storage capacity constraints
The reservoir storage in any period should not exceed its active storage capacity (Smax) of the reservoir.
St ≤ Smax t = 1, 2, 3, 4
St ≥ Smin t = 1, 2, 3, 4
Smax = Active storage capacity of reservoir in MCM. Smin = Dead Storage of the reservoir in MCM.
IV. RESULTS AND DISCUSSION
The important input variables in present GA model study are the monthly inflow in to the reservoir system and monthly irrigation demands for the month of June, July, August and September from year 2004 to 2006. After applying GA to the above model the following results are generated which gives the releases by GA and that we consider as optimum releases for the year 2004 to 2006.
Table 1: Demand, Actual Release and Obtained Release by GA for the year 2004 to 2006
Actual Release, MCM
Release by GA, MCM
1265.55 …show more content…
The releases developed by Genetic algorithm satisfy completely the irrigation demands for all the four months i.e. June, July, August and September from the year 2004 to 2006 respectively. The amount of water saved in the months of June, July and August for year 2004 to 2006 is 318.25 MCM, 1319.6 MCM and 897.78 respectively. Thus, almost in nine out of twelve months the optimal releases obtained by genetic algorithm, are less than the actual releases, which leads to considerable amount in saving of