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Water quality prediction is an important and widely studied topic since it has significant impact on national or regional ecological and water resources management. Due to water quality indicators series nonlinearity and non-stationary, the accuracy of conventional mostly used methods including regression analysis, ARIMA and neural network has been limited. The use of support vector machine has been...
A predictive model of water-quality, which based on wavelet transform and support vector machine, is proposed. This model uses wavelet transform to get water time sequence variations in different scale, and optimizes three parameters of Regression Support Vector Machine with improved Particle Swarm Optimization algorithm, to improve the accuracy of prediction model. This model is used to take one-step...
This paper studies the relation between chlorophyll-a and 10 environmental factors such as water temperature (T), COD, NH4+, NO3- TN, PO43+, TP, suspend solids (SS), Secci-depth (SD) and water depth (D) based on the monitoring data of 2005 in Taihu Lake. Three kinds of models are designed using the multiple regression statistical (MRS) method, the back propagation artifical neural network (BP ANN)...
According to the monitoring and analysis of chloroform in the water distribution network in a northern city of China, the variations of chloroform in the water distribution network and the major influence factors were studied. Using principle component analysis method, the prediction model which including 9 water quality indexes was established to predict the concentration of chloroform and the average...
The traditional method of detecting Chironomid larvaes and plankton mostly is manual identification, which is inefficient. This paper puts forward the Chironomid larvae recognition method which is based on the support vector machines. The Chironomid larvae images are decomposed using a wavelet from which the features were extracted and kernel function is used. The experiment shows that the recognition...
This paper deals with the study of a water quality prediction model through application of LS-SVM in Liuxi River in Guangzhou. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome...
It has been a more complex problem for water quality assessment. And its aim is to well and truly evaluate its degree of pollution for bodies of water, which will be easy to provide some principled projects and criterions for water resource's protection and their integration application. So, it has been widely applied into water quality assessment. SVM and directed acyclic graph support vector machine...
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