Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM (1,1) model

Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model

Accurate prediction of the dissolved oxygen level (DOL) is important for enhancing environmental conditions and facilitating water resource management. However, the irregularity and volatility inherent in DOL pose significant challenges to achieving precise forecasts. A single model usually suffers from low prediction accuracy, narrow application range, and difficult data acquisition. This study proposes a new weighted model that avoids these problems, which could increase the prediction accuracy of the DOL. The weighting constructs of the proposed model (PWM) included eight neural networks and one statistical method and utilized Young's double-slit experimental optimizer as an intelligent weighting tool. To evaluate the effectiveness of PWM, simulations were conducted using real-world data acquired from the Tualatin River Basin in Oregon, United States. Empirical findings unequivocally demonstrated that PWM outperforms both the statistical model and the individual machine learning models, and has the lowest mean absolute percentage error among all the weighted models. Based on two real datasets, the PWM can averagely obtain the mean absolute percentage errors of 1.0216%, 1.4630%, and 1.7087% for one-, two-, and three-step predictions, respectively. This study shows that the PWM can effectively integrate the distinctive merits of deep learning methods, neural networks, and statistical models, thereby increasing forecasting accuracy and providing indispensable technical support for the sustainable development of regional water environments.

Examination and Forecast of Relationship among Tourism, Environment, and Economy: A Case Study in Shandong Province, China

Correctly understanding and handling the relationship of tourism industry, ecological environment, and regional economy is an important prerequisite and foundation for realizing regional ecological protection and high-quality development. Based on the entropy method and the coupling coordination model, this paper conducts quantitative research on the coupling coordination relationship and development law of tourism industry-ecological environment-regional economic (TEE) in various cities in Shandong Province. First, a coupling coordination evaluation system of TEE was constructed to evaluate the comprehensive development level of the three systems in each city in Shandong Province from 2010 to 2017; secondly, based on the coupling coordination model, the relationship among the three systems of each city was analyzed using spatial and temporal dimensions; finally, the gray GM (1, 1) model was used to predict the future coupling coordination degree of the three systems in Shandong Province. The research results show that: (1) the development of the economy and tourism industry of cities in Shandong Province is highly correlated, and the overall trend is increasing. The ecological environment mainly changes first, and then rises. (2) From the perspective of time, the changes in the coupling coordination degree of the three systems are mainly to maintain stability and increase fluctuations, and generally develop in the direction of benign coordination. From a spatial perspective, the coupling coordination degree of the three systems shows significant regional integrity and differences, showing a pattern of high in the east and low in the west. (3) In the next few years, the coupling coordination degree of the three systems will roughly continue the characteristics of changes from 2010 to 2017.

Using a fractional order grey seasonal model to predict the dissolved oxygen and pH in the Huaihe River

To accurately forecast the seasonal fluctuations of dissolved oxygen (DO) and pH in Huaihe River, a grey seasonal model with fractional order accumulation is proposed, optimized by particle swarm optimization (PSO-FGSM(1,1)). We use this new model to carry out an empirical analysis based on the DO and pH data from 2014 to 2018 from Huaibin, Bengbu, Chuzhou monitoring points. The comparison results show that the PSO-FGSM(1,1) model accuracy is significantly higher than the Holt-Winters model with grey wolf optimization (GWO-Holt-Winters). The prediction results indicated that the pollution of the Huaihe River has regional characteristics. The Huaibin and Chuzhou sections of the Huaihe River are slightly polluted, and the Bengbu section is seriously polluted.

Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model

The new energy vehicles (NEVs) industry has been regarded as the primary industry involving in the transformation of the China automobile industry and environmental pollution control. Based on the quarterly fluctuation characteristics of NEVs’ sales volume in China, this research puts forwards a data grouping approach-based nonlinear grey Bernoulli model (DGA-based NGBM (1,1)). The main ideas of this work are to effectively predict quarterly fluctuation of NEVs industry by introducing a data grouping approach into the NGBM (1,1) model, and then use the particle swarm optimization (PSO) algorithm to optimize the parameters of the model so as to increase forecasting precision. By empirical comparison between the DGA-based NGBM (1,1) and existing data grouping approach-based GM (1,1) model (DGA-based GM (1,1)), DGA-based NGBM (1,1) can effectively reduce the prediction error resulting from quarterly fluctuation of sales volume of the NEVs, and prediction performance are proven to be favorable. The results of out-of-sample forecasting using the model proposed show that the sales volume of NEVs in China will increase by 57% in 2019–2020 with a quarterly fluctuation. In 2020, the sales volume of NEVs will exceeds the target of 2 million in the “13th Five-Year Strategic Development Plan”. Therefore, China needs to pay more attention to infrastructure construction and after-sales service for NEVs.