| A study on the modified ANFIS model by the Calman filter for ET0 prediction |
|
View Fulltext View/Add Comment Download reader |
| |
| DOI：10.7606/j.issn.1000-7601.2017.03.18 |
| Key Words: reference crop evapotranspiration Penman-Monteith formula ANFIS model Calman filter prediction accuracy |
| Author Name | Affiliation | | LI Zhi-lei | Mechanical Engineering College of Xinjiang University, Urumqi, Xingjiang 830000, China | | ZHOU Jian-ping | Mechanical Engineering College of Xinjiang University, Urumqi, Xingjiang 830000, China | | WEI Zheng-ying | State Key Laboratory of Mechanical Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China | | ZHANG Yu-bin | State Key Laboratory of Mechanical Manufacturing System Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China | | XU Yan | Mechanical Engineering College of Xinjiang University, Urumqi, Xingjiang 830000, China |
|
| Hits: 1006 |
| Download times: 668 |
| Abstract: |
| Real time and accurate prediction for water demand by crop is the key technology to realize intelligent water-saving irrigation. The reasonable selection of forecasting model and the improvement of accuracy is the key to the decision–making system on water demand. This article introduced the meteorological data on environmental information in Xi'an of Shaanxi province to the forecast model of self adaptive neural fuzzy inference (ANFIS) reference crop transpiration (ET0). The calman filter was used to filter the noise of the ET0 value obtained by the ANFIS model to improve the forecasting accuracy of the model, thus improving the forecasting accuracy of model and verifying the accuracy of the model through simulation and experiment. The simulation results showed that the equal coefficient(EC) reflecting the fitting degree between the real value and the result of forecasting model was 0.93 and 0.98 after being adjusted. The results from experiment showed that the ANFIS forecast model's mean absolute error was 28.94%, and the average relative error was 26.37%. After modification, the mean absolute error was 7.24%, and the average relative error was 6.59%. Simulation and experimental results indicated that the prediction model of ANFIS was modified by using calman filter, which could improve the accuracy of prediction. The revised ANFIS model by the calman had better reflection of the change trend of ET0. |
|
|
|