Fig. 1.1 Storage and water quality single outlet, and quality within the reservoir. research are given in the following sections. Fig. 2.1 Proposed methodology for the water quantity Fig. 1.2 Basic components of the Barra Bonita inflow and release act as key factors on the system behavior. Fig. 2.2 Modeling the Barra Bonita system Fig. 3.2: Location of Barra Bonita reservoir the Tieté River can be seen in Fig. 3.2. Fig. 3.1 Schematic profile of the Barra Bonita dam storage levels and elevations is presented in Fig 3.1. Fig. 4.1 Basic structure of the ANN water quality ANN model with GA training is shown in Fig. 4.2. Fig. 4.2 Flowchart for the overall model construction Fig. 4.3c Results for DO after training Fig. 4.3b Results for chlorophyll after training Fig. 4.3a Results for phosphorous after training used in the training process. Fig. 5.2 Membership for the fuzzy number A(a, b, c) Fig. 5.5a BOD load into Tiete River Fig. 5.4b TN concentration of Piracicaba River Fig. 5.3a BOD load into Piracicaba river Fig. 5.3b TN loads into Piracicaba river Fig. 5.4a BOD concentration of Piracicaba River Fig. 5.6a BOD concentrations of Tiete River Fig. 5.5b TN load into Tiete River Fig. 5.6b TN concentrations of Tiete River Fig. 6.1 Fuzzy membership functions for flow of release and produced energy, respectively It is very important, particularly in the case of Fig. 6.2 Fuzzy membership functions for water can be found in Bollman and Marques (2000). quality evaluation within the reservoir Fig. 6.4 Guidecurves for the month of November for the month of November is showed in Fig. 6.4. Fig. 7.5a Optimized storage for low load situation large variation of release values. Fig. 7.5b Optimized storage for high load situation Fig. 7.7a Results of flow stabilization fuzzy data collection process. hydropower generation, respectively. Fig. 7.6b Results of TP for high load situation Fig. 7.7b Results for the hydropower fuzzy evaluation
