Evaporation Estimation using Artificial Neural Networks: Based on (I) Dynamic Factor Analysis and (II) Satellite Images
Date Issued
2011
Date
2011
Author(s)
Sun, Wei
Abstract
Evaporation is one of the major elements in the hydrological circle and an important reference to the management of water resources and agricultural irrigation. To efficiently explore the mechanism and spatial distribution of evaporation, the study consisted of two parts, in which the first part proposed a hybrid model (BD) combining Back-Propagation Neural Networks (BPNN) and Dynamic Factor Analysis (DFA) to improve the accuracy of evaporation estimation, and the second part made use of the satellite images to establish the spatial distribution of evaporation covering whole Taiwan.
In the first part, the DFA was first applied to investigate the influence of meteorological variables on evaporation. In addition, the common trend extracted from evaporation observations at each gauging station was obtained by evaluating the corresponding AIC (Akaike’s information criterion) values. Furthermore, the explanatory meteorological variables highly related to evaporation were also identified through the DFA. Finally, the BPNN was used for accurately estimating evaporation based on the selected explanatory meteorological variables and DFA estimation, and the performance of the constructed BD model was compared with that of empirical formulas. Results demonstrated that the proposed BD model has excellent applicability and reliability in terms of the accuracy of evaporation estimations.
The second part aims to construct an effective evaporation estimation model that possesses the ability to present the spatial distribution of evaporation in Taiwan. To achieve this goal, the remote sensing images obtained from Landsat 5 and Landsat 7 satellites were used as inputs to the Adaptive Network-Based Fuzzy Inference System (ANFIS). The image products included Enhanced Vegetation Index (EVI) and surface temperature with a sample size of 342. Results obtained in this phase indicated that the ANFIS model can easily perform the variation of evaporation estimations in space and accurately capture the trend of evaporation with errors of about 1 mm/day, which is acceptable for relative applications. Overall, the estimations of evaporation were achieved in this study in the aspect of point and regional estimations through BD and ANFIS approaches, respectively. The performance demonstrated that both models are of great stability and reliability in evaporation estimation, which are capable of providing valuable information for water resources management.
Subjects
Evaporation
Artificial Neural Network (ANN)
Dynamic Factor Analysis (DFA)
Adaptive Network-Based Fuzzy Inference System (ANFIS)
Backpropagation Neural Network (BPNN)
Enhanced Vegetation Index (EVI)
Landsat.
Type
thesis
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