MATLAB Simulation of Water Quality Prediction Based on SVM and Chaos Theory

In our graduation project, we investigated the integration of Support Vector Machine (SVM) and chaos theory for predicting water quality variations. This approach is particularly suitable for time series data with nonlinear characteristics, as water quality indicators such as dissolved oxygen and ammonia nitrogen concentrations often exhibit complex dynamic patterns that traditional linear models struggle to accurately capture.

The application of chaos theory primarily manifests in the phase space reconstruction of water quality data. By calculating time delay and embedding dimension parameters, one-dimensional water quality sequences are mapped to high-dimensional phase space, thereby revealing the underlying dynamical characteristics hidden within the data. This step provides richer feature information for subsequent modeling.

SVM was selected as the core prediction model due to its exceptional nonlinear regression capabilities. Through kernel functions (such as RBF), low-dimensional nonlinear problems are transformed into high-dimensional space for solution, while utilizing the structural risk minimization principle to avoid overfitting. During model training, special attention must be paid to optimizing the penalty factor C and kernel parameter γ, as these significantly impact prediction accuracy.

In the MATLAB implementation, the complete workflow can be divided into three key stages: First, chaos characteristic analysis (such as calculating Lyapunov exponents) is performed on raw water quality data to confirm its suitability for chaos modeling; followed by phase space reconstruction; finally, constructing the SVM prediction model and performing rolling multi-step predictions. The program determines optimal parameters through cross-validation and employs normalization processing to enhance data convergence.

Compared to single models, this hybrid approach offers two main advantages: chaos theory helps extract deep data features, while SVM's generalization capability ensures prediction stability. Experimental results demonstrate that this hybrid model exhibits stronger explanatory power for sudden water quality fluctuations, making it suitable for developing early warning systems in complex aquatic environments such as rivers and lakes.