Authors:
Zaiwen Liu
;
Xiaoyi Wang
and
Wei Wei
Affiliation:
College of Computer and Information Engineering and Beijing Technology and Business University, China
Keyword(s):
Modelling, Integrated nutritional index, Evaluation method，Rough set, Water Bloom Prediction， Least Squares Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
An integrated evaluative function and intelligent prediction model for water bloom in lakes based on least squares support vector machine( LSSVM) is proposed in this paper, in which main influence factor of outbreak of water bloom is analyzed by rough set theory. First the study of the function involves three aspects: algal average activation energy of photosynthesis, integrated nutritional status index, and transparency, which are considered from the microcosmic level., the macroscopic level and the intuitionistic level respectively. The values of the function are classified properly. At the meantime, the weight value of each evaluative parameter is determined objectively, via the theory of multiple criteria decision making,. By analyzing and calculating the experimental data, the obtained values of the function and the classification results can be verified using the data of the samples. Good agreement is obtained between the results and the fact. The results of simulation and app
lication show that: LSSVM improves the algorithm of support vector machine (SVM).; it has long-term prediction period, strong generalization ability， high prediction accuracy; and needs a small amount of sample and this model provides an efficient new way for medium-term water bloom prediction.
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