This paper introduces the application of a combined forecasting model based on inverse error weighting method in urban air quality prediction. Compared with other forecasting methods, the weight of the proposed method is determined by the proportion of the inverse of the sum of squared errors in the whole sum of squared errors, so that the weight of the model with higher accuracy is larger and the prediction result is more accurate. In this paper, three single forecasting models, multiple regression model, time series model and LSTM model, were established. Since the three forecasting models have different advantages and disadvantages and different forecasting results, this paper used different weights for the three models based on the combined advantages and according to the forecasting results. The three models were then weighted by using the error inverse weighting method. Then, the prediction results of the three models were assigned different weights. Finally, the combined prediction results were obtained.

Air Quality Prediction Based on Multivariate Variable Weight Combined Prediction Model

Wireless communication has become a preferred direction for the development of layered water injection tools due to its low cost and high reliability. However, the wireless system relies on the underground battery for power supply，and each communication will consume a significant amount of energy. In order to save energy consumption, the wireless system adopts the intermittent sleep communication mode, with intervals of usually more than one month. During the idle time of communication, the downhole parameters such as pressure and flowrate will change as the pressure and flowrate at the wellhead. Therefore, it is crucial to predict downhole parameters based on the wellhead pressure and flowrate. In this study, a downhole parameter prediction method based on multi-layer water injection model is proposed. A multilayer injection prediction model was established based on the hydraulic analysis of the tubing string, and the model parameters were identified and updated using the historical data uploaded each time. The pressure and flow rate measured at the wellhead were used as inputs to the model, and the recursive relationship between layers in the multilayer model was utilized to predict downhole parameters for each layer. A model parameter optimization method based on time-weighting is proposed in order to address the gradual changes in model parameters during water injection. This method assigns greater weight to more recent historical data, resulting in optimized model parameters. Experimental results show that the proposed method can effectively predict the flowrate and pressure of each layer, with a prediction deviation of less than 5% F.S., which provides technical support for the application and popularization of the wireless layered water injection system. 

Downhole parameter prediction method based on multi-layer water injection model and historical data-based model parameter identification

Prediction of Comprehensive Water Cut in Periodic Waterflood Reservoir Based on Variable Weight Combination Model

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Prediction of Comprehensive Water Cut in Periodic Waterflood Reservoir Based on Variable Weight Combination Model