Water Pollution Prediction Based on Deep Belief Network in Big Data of Water Environment Monitoring

Aiming at the problems that the traditional water quality prediction model is generally not high in prediction accuracy and robustness, a water pollution prediction using deep learning in water environment monitoring big data is proposed. Objective. To optimize and improve the prediction accuracy of the water quality prediction model. Firstly, in the water environment monitoring system, the Internet of Things big data technology is used to accurately sense and monitor the real-time data of sewage treatment equipment and sewage quality. Then, the deep belief network (DBN) is used to build the water pollution prediction model, and the collected sewage treatment data is analyzed to predict the water quality status. Finally, particle swarm optimization algorithm is used to dynamically optimize the number of hidden layer neural units and learning rate in the DBN prediction model, which makes the prediction results more scientific and accurate. Based on the sampling data of Shanghai Jinze Reservoir, the proposed model is experimentally analyzed. The results show that the probability of accurate location of the pollution source is not less than 70%. And under the two indicators of chemical oxygen demand and biological oxygen demand, the root mean square error and correlation coefficient are 3.073, 0.9892 and 1.958, 0.9565, respectively, which are better than other comparison models.

Air Pollution Forecasting Using Deep Learning

Nowadays, air pollution is getting an extreme problem that affects the whole environment. Due to the dangerous effects of air pollution on human’s health, this study proposes an air pollution prediction system. Because of the high dust pollution in Saudi Arabia, and the fact that there is no system for predicting the percentage of air pollution in it, this study applies an air pollution prediction system to the most affected area in Saudi Arabia. This paper aims to forecast the concentrations of PM10 particles due to their dangerous effects. This study aims to align with the Saudi vision 2030 by having an ideal environment and act in an efficient way in case of a warning situation. It applies a deep learning technique, which called Long Short-Term Memory (LSTM) to predict the air pollution in Saudi Arabia and achieved exceptional results due to the low error rates that have been obtained by this study. The error rate of Mean Absolute Error (MAE) is 0.98, for Root Mean Square Error (RMSE) is 8.68 and 0.999 for R-Squared.