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Figure 5.21. Optimal monthly conveyed water (CW) from the Gavoshan Dam to the Miandraband plain and optimal monthly groundwater withdrawal (GW) used as input in the hybrid wavelet-ANFIS
model to predict the GL.
The results of these GL- predictions are shown in Figure 5.22, together with the 95%- confidence interval computed using the RMSE obtained earlier in the testing phase of the hybrid wavelet-ANFIS model (see Figure 5.20). Also drawn in the figure are the critical maximum (water-logging) (about 1m below the land surface) and the minimum GL-threshold levels (see also Figure 5.4). Figure 5.22 clearly indicates that the computed optimized CW- and GW- volumes are such that the groundwater levels stay well within the allowed range, i.e.
neither high water-logging- prone- nor unacceptably low groundwater tables will occur.
Figure 5.22. Simulated monthly GL using optimal monthly inputs for CW and GW with 95% -confidence interval. Also shown are the critical maximum (waterlogging) (about 1m below the land
surface) and the minimum GL-threshold levels (not to scale).
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(GW) for making up the difference between irrigation water deficit (IWR) and the optimal amount of surface water conveyed by the Gavoshan dam into the plain on the groundwater levels have been estimated.
The long term time series of monthly river discharge were used for calculating the probability of occurrence of dam inflow using the two-parameter Weibull distribution and non-parametric Weibull plotting position method. The mean values of the last 10 years show that the best monthly data for water resources planning problems is the dam inflow at the 70%
minimum exceedence probability level estimated with the parametric model.
In order to find the optimal release from the Gavoshan dam a constrained optimization model based on objective demands as set by the Iranian government in the 1990’s and constraints of the long-term observed dam inflow above has been developed and solved by the method of Particle Swarm Optimization (PSO). The optimal amount of conveyed water (CW) from the dam into the Miandarband plain has then been calculated based on the politically prioritized proportions of the dam’s allocated water for domestic, environmental and agricultural uses.
The estimation of the irrigation water request (IWR) calculated by the FAO-56 method and using empirical crop coefficients of the present agricultural pattern in the plain indicates that the original recommended irrigation water request (RIW) has been extremely underestimated and both cannot be satisfied by neither the observed nor the optimal CW. Therefore, this irrigation water deficit must be made up for by groundwater withdrawal (GW), however, at the risk of unacceptable changes in the groundwater levels (GL).
Using the hybrid Wavelet-ANFIS/FCM model, monthly groundwater levels (GL) are functionally connected to the monthly observed CW as well as to the estimated GW (equal to the surface water deficit) and this input-output relationship trained and tested. The statistical analysis of the training and testing results shows that the hybrid model works appropriately.
In the final step optimal CW and corresponding GW are used as input predictors in the trained hybrid model to get corresponding GL. The latter are then checked if they violate either the upper water-logging threshold or the lower limit of a too severe drop of the groundwater table. If so, the GW must be adjusted accordingly in an iterative process which, in turn, means am associated alteration of the conjunctive surface-groundwater management in the plain.
However the results obtained so far do not hint of such a possibility, as the groundwater levels computed under optimal conditions stay well within the given bounds.
In conclusion, the innovative coupled hybrid Wavelet-ANFIS/FCM- PSO model developed here reveals itself to be a helpful tool for developing efficient conjunctive surface-groundwater resources management systems, in particular, whenthere is a lack of data, and/or when the physical processes of surface-groundwater interactions are not completely understood, so that deterministic physical models are barely applicable.
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