Aims: Develop a novel traffic flow prediction model to improve the accuracy of traffic flow prediction, better assist intelligent traffic management, improve traffic efficiency, reduce traffic congestion, and thus better improve sanitation and quality of life. Study Design:  Develop an urban road traffic flow prediction model with strong predictive power and excellent stability. Place and Duration of Study: Southwest University of Science and Technology, between July 2021 and March 2022. Methodology: Adopting wavelet threshold to denoise, first decompose the original data, then perform noise filtering on the subsequences obtained after decomposing, and finally reconstruct the denoised data. Use denoised data to train a multilayer perceptron and make predictions on future data. At the same time, several representative models are selected to compare with the proposed model to verify the competitiveness of the proposed model. Results: The proposed model has the smallest prediction error in the two training sets with different temporal granularity. In addition, we are using the data after wavelet denoising for training and prediction results in a smaller prediction error than using the data without denoising. Conclusion: The proposed prediction model has strong prediction ability and generalization performance in the field of traffic flow prediction. The wavelet denoising method can effectively improve the prediction accuracy of traffic flow prediction.

Water is the source of life, and the growth of animals and plants cannot leave the water source. The quality of water will directly affect the life and health of humans, animals and plants. In order to predict the concentration and changing trend of various pollutants in water bodies and promote the comprehensive management of water resources, this paper proposes a new integrated model based on the idea of Stacking integrated learning. The model is based on XGBoost, support vector regression, and multi-layer perceptron. The model is constructed with ridge regression as the meta-model. The model was applied to the pH and total nitrogen content of water quality, and the mean absolute percentage error was used to quantitatively evaluate the prediction results, and the results of the ensemble learning model were compared with the prediction results of a single base model. The results show that the stacking ensemble learning idea can effectively improve the prediction ability and generalization performance of the base model. The proposed ensemble learning model has very good prediction ability and generalization ability, and has great potential in other prediction fields such as water quality.

With the development of society and economy, people's living standards are improving day by day. The number of private cars is increasing, and the problem of urban traffic congestion is becoming more and more serious. Short-term traffic flow prediction is crucial to assist intelligent transportation system decision-making, solve congestion problems, and improve road capacity. In order to effectively improve the prediction accuracy and improve the generalization performance of the model, this paper combines extreme gradient boosting (XGBoost) and harris hawk optimization (HHO) to propose a multi-step prediction hybrid model. When building a hybrid model, the hyperparameter selection of the XGBoost model is converted into an optimization problem, and the optimization problem is solved through HHO. The solution to the final optimization problem is the optimal parameter combination of the XGBoost model. In order to verify the performance and competitiveness of the model, this study applies the proposed model to traffic flow prediction together with seven other representative models. The results show that the model has high accuracy and stability in practical applications.