| Cite this article: | 樊红梅,刘晓民,王文娟,等.基于多种方法的“以电折水”系数研究[J].灌溉排水学报,0,():-. |
| fanhongmei,liuxiaomin,wangwenjuan,et al.基于多种方法的“以电折水”系数研究[J].灌溉排水学报,0,():-. |
|
| |
|
|
| This article has been：Browse 1151Times Download 0Times |
|
|
|
| DOI： |
|
| Research on the coefficient of “Conversion of Electricity to Water” based on various methods |
|
fanhongmei1,2, liuxiaomin1, wangwenjuan2, chenqiong1
|
|
1.Inner Mongolia Agricultural University;2.Inner Mongolia Jinhuayuan Environmental Resources Engineering Consulting Co., Ltd
|
| Abstract: |
| Abstract:【Background】Agriculture is the largest economic and social water sector in China. At present, the main problem and primary difficulty of agricultural water management is to solve the problem of agricultural water measurement and accounting. In recent years, according to the characteristics of agricultural water use and the problems existing in the process of water use, the method of "converting electricity into water" is introduced to measure agricultural water consumption.【Objective】In view of the high proportion of agricultural water and the difficulty of measurement and monitoring in China, this paper introduces the method of "converting electricity into water" to measure agricultural water, so as to promote the measurement and accounting of agricultural water. 【Method】Taking Horqin District of Tongliao City in Inner Mongolia as an example, the average value prediction, multiple linear regression model and BP neural network model are used to predict and analyze the coefficient of "converting electricity into water", and the average relative error (MRE), root mean square error (RMSE) and decisive coefficient (R2) are selected to optimize the above three models.【Result】The results show that the average prediction error of the average value is 7.40%, and the fitting effect between the predicted value and the measured value is poor; The average prediction error of multiple linear regression model is 2.40%, and the fitting effect between predicted value and measured value is good; The average prediction error of BP neural network model is 1.65%, and the fitting effect between the predicted value and the measured value is good.【Conclusion】The MRE, RMSE and R2 of multiple linear regression model are 0.024, 0.175, 0.923 and 0.018, 0.131, 0.957 respectively, and the average prediction model has the worst prediction accuracy. |
| Key words: “conversion of electricity to water”coefficient; Average value prediction; Multiple linear regression; BP neural network |
|
|
|
|
|
|
