Research On Artificial Neural Networks (ANN) For Water Quality Assessment And Simulation

Innovative work is done on modeling and algorithm of artificial neural networks (ANN) for water quality assessment and simulation in this dissertation.Artificial neural network plays a leading role in the sciences for complex non-linear phenomena and artificial intelligence. Researches on its application in the planning of water pollution control are still in the preliminary stage in the world. On the basis of a comprehensive evaluation and analysis of the present situation of the researches in water quality assessment and simulation, and on the basis of a careful exposition of the basic principles, the algorithm and the varied pattern features of ANN, this dissertation gives an application of ANN approaches in water quality assessment and simulation, which, as the first attempt of its kind, can help to achieve a higher level in the application of artificial intelligence in this field.Based on the features of integrated water quality assessment, and the outstanding pattern recognition capacity of Hopfield network, a rational design of the structure of Liapunov energy function, this dissertation proposes the first Hopfield network model for comprehensive water quality assessment, and gives a strict mathematical deduction of the sample-classification performances of this model. Case study reveals that with numerous assessment indexes, Hopfield model can quickly produce assessment results with a high accuracy, and, as it can work with both qualitative and quantitative indexes, has wider areas of application.This dissertation combines Fuzzy Mathematics and ANN and produces a membership degree Back-Propagation network (MDBP) for water quality assessment compatible with the fuzzy features of water quality data. Case studies of the results of this model approach, fuzzy integrated index approach, and grey accumulation approach indicate: the proposed MDBP model combines the merits of ANN approach and fuzzy approach, and improves the accuracy and reliability of the assessment results while the results of fuzzy colligation index approach and grey accumulation approach are too high and too low respectively; it has a higher flexibility than conventional integrative index approach and its programs have a better adaptability and more convenient application; its ways of assessment are closer to the reality since it takes into consideration the continuity of the changes of environment water quality.Based on an analysis of the section-transmission features of the transference of thepollutants in the rivers, this dissertation creates a series ANN model, an improved ANN model, to simulate the transference of the pollutants in the rivers. In addition, it establishes a model of BOD-DO coupling for water quality simulation based on BP network, and a one-dimensional BP network model with its learning rate using Delta-Bar-Delta (DBD) self-adaptation technology. Case studies show a higher accuracy in the simulated results of ANN model over the one-dimensional model, thus proves the effectiveness, correctness and a bright future for the application of the proposed series ANN model structure.To overcome the tendency towards oscillation of the BP network near the minimum value, Chapter 8 proposes a one-dimensional radial basis function (RBF) network model for comprehensive water quality simulation for optimizing structure with algorithm, which is a useful exploration into issues such as the self-adaptive optimization of network structure through training samples. Case studies show that this RBF network has a higher generalization capacity in water quality simulation, thus improves the prediction precision of comprehensive water quality simulation with ANN.The numerical value solution of the two-dimensional mathematical model for water quality inspires a broad-sense network method for two-dimensional water quality simulation. This method takes into consideration the characteristics of the three aspects in two-dimensional water quality dispersion, and constructs input neurons of different expressions, the topological structu...