A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

Most of the water quality indicators that affect the results of river water quality assessment are gray and localized, thus the correlation between water quality indicators can be calculated using gray correlation analysis (GRA).However, GRA takes equal weighting for water quality indicators and does not take into account the weighting of the indicators. Therefore, this paper proposes a river water quality assessment method based on improved grey correlation analysis (ACGRA) andparticle swarm optimization multi-classification support vector machine (PSO-MSVM) for assessing river water environment quality. Firstly, the combination weights of water quality indicators were calculated using Analytic Hierarchy Process (AHP)AHP and Criteria Importance Though Intercrieria Correlation (CRITIC)CRITIC, and then the correlation between water quality indicators was calculated for feature selection. Secondly, the PSO-MSVM model was established using the water quality indicators obtained by ACGRA as input parameters for water environment quality assessment. The river water environment assessment methods of ACGRA and PSO-MSVM were applied to the evaluation of water environment quality in different watersheds in the country. Accuracy, precision, recall and root mean square errorRMSE were also introduced as model evaluation criteria. The results show that the river water environment assessment methods based on ACGRA and PSO-MSVM can evaluate the water environment quality more accurately

Research on water quality spatiotemporal forecasting model based on ST-BIGRU-SVR neural network

With the serious deterioration of the water environment, accurate prediction of water quality changes has become a topic of increasing concern. To further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new water quality spatiotemporal forecast model to predict future water quality. To capture the spatiotemporal characteristics of water quality pollution data, the three sites (station S1, station S2, station S4) with the highest temperature time series concentration correlation at the experimental sites were first extracted to predict the water temperature at station S1, and 17,380 records were collected at each monitoring station, and the spatiotemporal characteristics were extracted by BiGRU-SVR network model. This paper's prediction test is based on the actual water quality data of the Qinhuangdao sea area in Hebei province from 2 September to 26 September 2013 and compared with other baseline models. The experimental results show that the proposed model is better than other baseline models and effectively improves the accuracy of water quality prediction, and the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) are 0.071, 0.076, and 0.957, respectively, which have good robustness. HIGHLIGHT In order to capture the spatiotemporal characteristics of water pollution data, a new bidirectional gated recurrent unit networks and support vector regression model hybrid neural network model proposed in this paper focuses on water quality data trends and contextual temporal attributes to capture the spatiotemporal characteristics of water pollution data.; Multiparameter water quality prediction is realized, with comprehensive consideration of the correlations between data.

Adaptive Fuzzy Sliding Controller with Dynamic Compensation for Multi-Axis Machining

The precision of multi-axis machining is deeply influenced by the tracking error of multi-axis control system. Since the multi-axis machine tools have nonlinear and time-varying behaviors, it is difficult to establish an accurate dynamic model for multi-axis control system design. In this paper, a novel adaptive fuzzy sliding model controller with dynamic compensation is proposed to reduce tracking error and to improve precision of multi-axis machining. The major ad-vantage of this approach is to achieve a high following speed without overshooting while maintaining a continuous CNC machine tool process. The adaptive fuzzy tuning rules are derived from a Lyapunov function to guarantee stability of the control system. The experimental results on GJ-110 show that the proposed control scheme effectively minimizes tracking errors of the CNC system with control performance surpassing that of a traditional PID controller

The International Linear Collider Technical Design Report

The International Linear Collider Technical Design Report (TDR) describes in four volumes the physics case and the design of a 500 GeV centre-of-mass energy linear electron-positron collider based on superconducting radio-frequency technology using Niobium cavities as the accelerating structures. The accelerator can be extended to 1 TeV and also run as a Higgs factory at around 250 GeV and on the Z0 pole. A comprehensive value estimate of the accelerator is give, together with associated uncertainties. It is shown that no significant technical issues remain to be solved. Once a site is selected and the necessary site-dependent engineering is carried out, construction can begin immediately. The TDR also gives baseline documentation for two high-performance detectors that can share the ILC luminosity by being moved into and out of the beam line in a "push-pull" configuration. These detectors, ILD and SiD, are described in detail. They form the basis for a world-class experimental programme that promises to increase significantly our understanding of the fundamental processes that govern the evolution of the Universe