Real-time water quality prediction model based on IGA-BPNN method
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Abstract:
A surface w ater quality predictio n model is developed to analyze the inherent water quality variation tendencies and provides r eal-time early warnings according to the high-frequency water quality historical observation data. The developed water quality prediction model is integrated by an improved genetic algorithm ( IGA) and a Back Propagation Neural Network ( BPNN) . To verify the accuracy and reliability of the river water quality prediction model based on IGA-BPNN, the method is applied to the Potomac River in the United States to predict its water quality parameters, turbidity ( TURB) and conductivity ( SC) , and to analyze the performance of the prediction results. The prediction results demonstrate that the developed IGA-BPNN model can provide a more accurate prediction result than the BPNN model. Since the PIPC values of TURB and EC predictio n can reach 991.81% and 100% under normal stable conditions, GA2BPNN model also can provide a reliability prediction result. Meanwhile, the developed IGA-BPNN models can reflect the long period and iso lated sharp peaks of the water quality variations, and effectively provide real-time early warning for emergency response.
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