Studies On Decision Support Assessment System Of Water Environment Based On GIS

Water environmental assessment is a complicated large-scale system which involves many subjects and factors, and its qualities will directly influence human's daily life and economic development. Decision support system (DSS) provides a decision-making environment where we can analyze questions, construct models, and simulate the process and effects of decision; it has been an effective and powerful tool in solving some semi-structured and unstructured problems.With the rapid development of artificial intelligent technology and geography information system (GIS), decision support system not only solves quantitative problems but also deals well with the uncertain, fuzzy, qualitative information, and helps decision-makers to make sensible decisions. Facing the deficient water resource and seriousness of pollution, aiming at upgrading environmental management and promoting well-development of water resource, the researches about water environment assessment DSS design based on GIS are as follows:1. Study on remote sensing images compression technology. The statistical and relative characteristics of remote sensing images as well as some kinds of design methods in codebook are analyzed and studied. Depending on the spatial distribution characteristics and multi-resolution feature of wavelet coefficients, the vector quantization of codebook design in image compression has been push forward supported by genetic algorithm and annealing methods. The result shows that this codebook design method is able to achieve better image restoration quality, faster convergence speed, and better compression ratio than before.2. Researching on classification of remote sensing images. By means of remote sensing software, namely ERDAS IMAGING, the bond between unsupervised clustering algorithm and supervised classification has been strengthened through BP neural network, hence a higher precision of classification trials is got. Based on the self-organizing neural network and learning vector quantization algorithm, a hybrid learning vector quantization algorithm (HLVQ) combining the modified SOFM algorithm and the LVQ2 algorithm is proposed, and experimental results illustrate that HLVQ algorithm does well not only in retaining data topological structure but also in improving classification precision. In the end, the primary weaknesses of LVQ algorithm is discussed and GLVQ algorithm of remote sensing images classification is introduced, then classification models on the basis of the general learning vector...