Research On Soft-sensing Technique For Water Discharge At Lateral Gate Of The Irrigation Channel In The Yellow River

The continuous and automatic measurement of water discharge at lateral gate of the irrigation channel in the Yellow River and achievement of data remote transmission is one of important technical basis of implementing tradable water rights institution and digital irrigation area. The lateral gate of irrigation channel is specific reference to floodgate hydraulic building at main channel to branch canal in the Yellow River,the water discharge at lateral gate of the irrigation channel is legal base of water fee settlement between water management department and water users. Amongst all measurement methods for water discharge at lateral gate of irrigation channel, the method with floodgate hydraulic structure (the lateral gate) is the most economic one and has been used widely, because special discharge measuring facilities no longer been needed and hereby the flood-peak loss from the discharge measuring facilities can be avoided. Owing to many factors such as high content of mud and sand, heavy siltation, floating-debris, unsteady flow pattern and so on, the discharge at lateral gate of irrigation channel in the Yellow River is usually estimated by the water level at main channel (front of the floodgate), the water level at several points of branch canal (back of the floodgate) and the lift-height of floodgate. That is, the water discharge is calculated by the hydraulic measuring formula witch correspond with type of the hydraulic structure and present flow pattern as well as comprehensive discharge-coefficient from the in-situ calibration. This procedure has typical characteristics of soft-sensing.This dissertation is based on the fund project of national natural science (No.60165001)—The reseach on method and device for automatic measurement of water discharge at lateral gate of irrigation channel in the Yellow River. The following research works have been done under this fund project:(1) In order to realize tradable water rights institution and digital irrigation area in the Yellow River Basin, a overall scheme of remote discharge monitoring network based on SMS for irrigation channel in the Yellow River is designed, as important field unit, a kind of automatic measuring device for discharge at lateral gate of irrigation channel(ZL200420050359.1) is developed, including tow kinds of water level gauge (intelligent floater water lever sensor and Intelligent electric water-level rule) and measuring mechanism for the open position of floodgate matched with this device. For deep research on relevant problems about soft-sensing for discharge at lateral gate of irrigation channel as well as training and testing various soft-sensing models, a special reduced scale hydraulic model is designed and built.(2) Based on soft-sensing principle and engineering development specification, four technical items involved in soft-sensing for water discharge at lateral gate of the irrigation channel in the Yellow River are presented, they are secondary variables selecting, data preprocessing, soft-sensing model building and on-line calibration respectively. Firstly, water measuring formulas corresponding to specific flow patterns are involved into traditional mechanistic soft-sensing models and relevant secondary variables are determined. Based on the characteristic analysis of each water level signals, a comprehensive support degree based weighted average algorithm is proposed to effectively filter outliers and from random disturbance in water level measuring data. In view of special condition of lateral gate of the irrigation channel in the Yellow River, a rolling type on-line calibrating strategy for the soft-sensing models is also proposed.(3) In order to change soft-sensing technique based on traditional hydraulics mechanism into practical software, the soft-sensing expert system for discharge at lateral gate of the irrigation channel in the Yellow River is designed and implemented. The meticulous calculation of overall discharge-coefficient proposed and achieved by knowledge base expansion improves estimating precision and adaptability of traditional mechanistic models to some extent.(4) BP soft-sensing model for discharge at lateral gate of the irrigation channel in the Yellow River is firstly built, trained and tested by the hydraulic experimental data, the effect of ANN in improvement of estimating precision is proved. In view of disadvantage of BP model such as slower learning speed, easily getting into local minimum and worse generalization performance, corresponding RBF soft-sensing model is built and parameters are optimized by adaptive genetic algorithm(AGA) . The simulation results show that RBF soft-sensing model is more applicable than RBF one. In order to adapt embedded environment and discharge real-time estimation, a kind of hybrid soft-sensing model based on CMAC and hydraulics mechanism is built. Similarly, to meet the variety of flow patterns and the sparseness of training samples at lateral gate of the irrigation channel in the Yellow River, the strategy of dividing and ruling is proposed yet, hybrid soft-sensing expert network suitable to flow pattern variation is built and soft-sensing molding for all flow patterns is changed to soft-sensing molding only for single flow pattern.(5) Aiming at sparseness of training sample in engineering practice, a soft-sensing model base on Support Vector Regression(SVR) is built and PSO based optimizing method for soft-sensing model parameters is adopted. Training and testing for SVR base soft-sensing model under same experimental data and different kernel function and loss function, the results show that SVR base soft-sensing model is suitable to sparseness of training sample and estimating precision is higher than BP and the RBF soft-sensing model, Moreover, generalization can be improved essentially.(6) The discharge measuring practice on spot and hydraulic experiments indicate that estimating error of discharge measuring formulas recent adopted will increase evidently when approaching the boundaries of two or more than two flow patterns. Owing to lack of training samples at the flow pattern boundaries, the generalization of various soft-sensing model based on machine learning will also decrease significantly. Based on the analysis of fuzzy characteristics of plow pattern boundaries at lateral gate of the irrigation channel in the Yellow River, flower pattern regional reference model and soft-sensing scheme combined overall discharge coefficients fuzzy inference system (FIS) and data fusion are put forward, this soft-sensing scheme has achieved some effects in improvement of estimating precision and applicability of soft-sensing technique for fuzzy flow pattern boundaries.Main research achievements and innovative points of this dissertation are included in following five aspects: Firstly, development of automatic measuring device for discharge at lateral gate of irrigation channel witch is suitable to the irrigation channel in the Yellow River Basin and ideal hardware platform is established for implement of soft-sensing technique; Second, foundation of soft-sensing expert system based hydraulics mechanism and meticulous calculation of overall discharge-coefficients, both a kind of regulations for soft-sensing programming is established and a simple effective method for improving discharge estimating is provided; Third, various soft computing method such as BP, RBF, CMAC, AGA, SVR and PSO are selected to build the soft-sensing models witch are suitable for embedded environment, real-time discharge estimation and training sample scarcity, the estimating precision as well as applicability and reliability of the soft-sensing models are fully improved; Fourth, a hybrid soft-sensing model based on fuzzy inference technique is proposed and established, the estimating precision and applicability of the hydraulics mechanism model are improved effectively; Fifth, a kind of flower pattern regional reference model made of definite regions and fuzzy boundaries is proposed and distance measure based multi-model data fusion technique is adopted, valuable exploration has been done in improvement of discharge estimating precision and soft-sensing technique applicability at flow pattern fuzzy boundaries.