MATLAB Simulation for Water Quality Prediction Using SVM and Chaos Theory

This MATLAB simulation program implements water quality prediction using Support Vector Machines (SVM) combined with chaos theory. The codebase contains detailed documentation covering algorithm selection rationale, parameter configuration guidelines, and implementation methodology. As part of my graduation project, the program features multidimensional data preprocessing modules, chaotic characteristic analysis components, and SVM regression models with kernel function optimization. The implementation includes phase space reconstruction for chaotic time series analysis, Lyapunov exponent calculation for system stability assessment, and SVM training with cross-validation techniques. Future enhancements will incorporate advanced feature selection algorithms, hybrid prediction models, and real-time data integration capabilities to improve prediction accuracy and system robustness. I welcome feedback and suggestions for further optimization, particularly regarding parameter tuning strategies and computational efficiency improvements. Researchers are encouraged to utilize this codebase as a reference framework for environmental forecasting studies or to extend its functionality for specific applications. Water quality prediction remains a critical research domain with significant implications for environmental protection and public health. This simulation tool provides actionable insights into water quality dynamics, enabling proactive resource management and ecological conservation measures. The program architecture consists of three main modules: 1) Data preprocessing and chaos identification unit handling missing value interpolation and noise reduction 2) Feature extraction and dimension reduction components 3) SVM prediction engine with multiple kernel options (RBF, linear, polynomial). Thank you for your interest in this research contribution.