Comparison of M5 Model Tree and Artificial Neural Network’s Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images

2016 ◽  
Vol 30 (9) ◽  
pp. 3063-3075 ◽  
Author(s):  
Ali Rahimikhoob
Keyword(s):  
Satellite Images ◽  
Reference Evapotranspiration ◽  
M5 Model Tree ◽  
Model Tree ◽  
Noaa Satellite ◽  
Artificial Neural ◽  
M5 Model

scholarly journals Comparative Study of M5 Model Tree and Artificial Neural Network in Estimating Reference Evapotranspiration Using MODIS Products

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Armin Alipour ◽  
Jalal Yarahmadi ◽  
Maryam Mahdavi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrological Cycle ◽  
Reference Evapotranspiration ◽  
Weather Data ◽  
Climatic Data ◽  
M5 Model Tree ◽  
Model Tree ◽  
Artificial Neural ◽  
M5 Model

Reference evapotranspiration (ETO) is one of the major parameters affecting hydrological cycle. Use of satellite images can be very helpful to compensate for lack of reliable weather data. This study aimed to determine ETO using land surface temperature (LST) data acquired from MODIS sensor. LST data were considered as inputs of two data-driven models including artificial neural network (ANN) and M5 model tree to estimate ETO values and their results were compared with calculated ETO by FAO-Penman-Monteith (FAO-PM) equation. Climatic data of five weather stations in Khuzestan province, which is located in the southeastern Iran, were employed in order to calculate ETO. LST data extracted from corresponding points of MODIS images were used in training of ANN and M5 model tree. Among study stations, three stations (Amirkabir, Farabi, and Gazali) were selected for creating the models and two stations (Khazaei and Shoeybie) for testing. In Khazaei station, the coefficient of determination (R2) values for comparison between calculated ETO by FAO-PM and estimated ETO by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, R2 values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate ETO by means of LST data derived from MODIS sensor.

Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree

2021 ◽  
Author(s):  
Ozgur Kisi ◽  
Behrooz Keshtegar ◽  
Mohammad Zounemat-Kermani ◽  
Salim Heddam ◽  
Nguyen-Thoi Trung
Keyword(s):  
Reference Evapotranspiration ◽  
M5 Model Tree ◽  
Model Tree ◽  
Radial Basis ◽  
M5 Model

scholarly journals Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: A Novel Approach

2021 ◽  
Author(s):  
Dilip Kumar Roy ◽  
Kowshik Kumar Saha ◽  
Mohammad Kamruzzaman ◽  
Sujit Kumar Biswas ◽  
Mohammad Anower Hossain
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Evaluation ◽  
Reference Evapotranspiration ◽  
Particle Swarm ◽  
Regression Tree ◽  
Superior Performance ◽  
M5 Model Tree ◽  
Swarm Optimization ◽  
Model Tree ◽  
M5 Model

Abstract Reference evapotranspiration (ET0) is a crucial element for deriving a meaningful scheduling of irrigation for major crops. Thus, precise projection of future ET0 is essential for better management of scarce water resources in many parts of the globe. This study evaluates the potential of a Hierarchical Fuzzy System (HFS) optimized by Particle Swarm Optimization (PSO) algorithm (PSO-HFS) to predict daily ET0. The meteorological variables and estimated ET0 were employed as inputs and outputs, respectively, for the PSO-HFS model. The FAO 56 PM method to ET0 computation was implemented to obtain ET0 values using the climatic variables obtained from two weather stations located in Gazipur Sadar and Ishurdi, Bangladesh. Prediction accuracy of PSO-HFS was compared with that of a FIS, M5 Model Tree, and a Regression Tree (RT) model. Several statistical performance evaluation indices were used to evaluate the performances of the PSO-HFS, FIS, M5 Model Tree, and RT in estimating daily ET0. Ranking of the models was performed using the concept of Shannon’s Entropy that accounts for a set of performance evaluation indices. Results revealed that the PSO-HFS model performed better than the tree-based models. Generalization capabilities of the preposed models were evaluated using the dataset from a test station (Ishurdi station). Results revealed that the models performed equally well with the unseen test dataset, and that the PSO-HFS model provided superior performance over other tree based models. The overall results imply that PSO-HFS model could effectively be utilized to model ET0 values quite efficiently and accurately.

scholarly journals Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

2021 ◽  
Author(s):  
Ozgur Kisi ◽  
Behrooz Keshtegar ◽  
Mohammad Zounemat-Kermani ◽  
Salim Heddam ◽  
Nguyen-Thoi Trung
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Reference Evapotranspiration ◽  
Climatic Data ◽  
M5 Model Tree ◽  
Model Tree ◽  
Radial Basis ◽  
Input Variables ◽  
M5 Model

Abstract In the current study, an ability of a novel regression-based method is evaluated in modelling daily reference evapotranspiration (ET0), which is an important issue in water resources management plans and helps farmers in irrigation planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The radial-based kernel function was used to control the input variables in modelling process of M5 model tree. The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM) and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (NSE > 0.997) followed by the MLPNN (NSE > 0.990), and M5Tree (NSE > 0.945) in modelling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modelling scenario revealed that taking into account of all variables would considerably increase models’ accuracies for the three stations.

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