Estimation of Daily Reference Evapotranspiration (ET0) in the North of Algeria Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) Models: A Comparative Study

被引：0
作者：
Ibtissem Ladlani
Larbi Houichi
Lakhdar Djemili
Salim Heddam
Khaled Belouz
机构：
[1] University Badji-Mokhtar Annaba,Hydraulics Department, Faculty of Engineering Sciences
[2] University Hadj Lakhdar Batna,Hydraulics Department, Institute of Civil Engineering
[3] University 20 Août 1955,Hydraulics and Architecture
[4] Superior National School of Agronomics of El Harrach (ENSA),Hydraulics Division, Agronomy Department, Faculty of Science
来源：
Arabian Journal for Science and Engineering | 2014年 / 39卷
关键词：
Reference evapotranspiration (ET; ); Adaptive Neuro-fuzzy inference system; ANFIS; Multiple linear regression; MLR; Penman method; Modelling;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The aim of this study is to model the daily reference evapotranspiration (ET0) in the Mediterranean region of Algiers, Algeria country, using Adaptive Neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) models. Various daily climatic data, i.e. daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature and wind speed obtained from Dar El Beida weather station, are used as inputs to the ANFIS and MLR models so as to estimate ET0. In order to find the optimal topology of the ANFIS, different architectures were trained and examined and the network with minimum Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and high Coefficient of Correlation (CC) has been selected as an optimal configuration. A comparison was conducted between the estimates provided by the ANFIS and by MLR. The results showed that ANFIS using the climatic data successfully estimated ET0 and the ANFIS simulated ET0 better than the MLR. Totally 2,193 daily samples were used for training the model, and 730 daily samples for testing and validation of the model. The developed ANFIS model for the ET0 modelling shows good performance with an MAE index in the range of 0.32–0.75, RMSE between 0.41 and 0.75 and the CC in the range of 0.80–0.96, which endows with high performance of predictive ANFIS system to make use for modelling daily reference evapotranspiration (ET0).
引用
收藏
页码：5959 / 5969
页数：10
相关论文
共 42 条
  • [1] Gocic M.(2011)Service-oriented approach for modeling and estimating reference evapotranspiration Comput. Electron. Agric. 79 153-158
  • [2] Trajkovic S.(2005)Prediction accuracy for project wide evapotranspiration using crop coefficients and reference evapotranspiration ASCE J. Irrig. Drain. Eng. 131 24-36
  • [3] Allen R.G.(2013)A simple method to directly retrieve reference evapotranspiration from geostationary satellite images Int. J. Appl. Earth Obs. Geoinf. 21 149-158
  • [4] Clemmens A.J.(2012)Artificial neural network models for reference evapotranspiration in an arid area of northwest China J. Arid Environ. 82 81-90
  • [5] Burt C.M.(2008)The potential of different ANN techniques in evapotranspiration modeling Hydrol. Process. 22 2449-2460
  • [6] Solomon K.(2009)Development and validation of GANN model for evapotranspiration estimation ASCE J. Hydrol. Eng. 14 131-140
  • [7] O’Halloran T.(2009)Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration Hydrol. Process. 23 874-880
  • [8] Cammalleri C.(2011)Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration Irrig. Sci. 29 431-441
  • [9] Ciraolo G.(2012)Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions Int. J. Biometeorol. 56 831-841
  • [10] Huo Z.(2007)Adaptive neurofuzzy computing technique for evapotranspiration estimation ASCE J. Irrig. Drain. Eng. 133 368-379
12345