Daily Pan Evaporation Modeling in a Hot and Dry Climate

Piri, Jamshid; Shirvani, Amin; Moghaddamnia, A.; Keshavarz, Alireza; Han, Dawei; Remesan, Renji

doi:10.1061/(asce)he.1943-5584.0000056

Cited by 104 publications

(47 citation statements)

References 53 publications

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“…Using the Gama Test, Remesan et al (2008) specified the effective factors on solar radiation in United Kingdom. Moghadamnia et al (2008) used this methodology to model the regional evaporation of half wells in Sistan and Baloochestaan;and Piri et al (2010) used this test to model the evaporation in warm and dry areas(Sistan and Baloochestan, Iran).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Chari¹,

Nemati²,

Afrasiab³

et al. 2012

JAS

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The relation between water table depth and evaporation rate from bare soil is of great importance in arid and semi-arid areas. In such areas, due to over irrigation, the water table is very close to the ground surface which leads to salinization of the soil. In this study a physical water table model was used to estimate evaporation rate in sandy loam, loam and clay loam soils under greenhouse conditions for 40, 60 and 80 cm water table depth. The evaporation from bare soil, evaporation from free surface, soil surface moisture (with using TDR) and maximum and minimum daily temperature were measured for 74 days in this study. In the next step, several nonlinear models have been efficiently developed with the aid of the Gamma test (GT), including local linear regression, two layer back propagation, and conjugate gradient descent and BFGS neural network to simulate the evaporation from soil. And finally, for evaluation of the models, the root mean square error and mean absolute error and larger determination coefficient were calculated. The results showed a suitable correlation between the predicted values and the test measures.

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Section: Introductionmentioning

confidence: 99%

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Chari¹,

Nemati²,

Afrasiab³

et al. 2012

JAS

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show abstract

“…In hydrological modelling, ANN has been widely used for simulating many hydrological processes such as rainfall runoff simulations (Han et al, 2007b) and evaporations Piri et al, 2009). There are a large number of articles and books available on ANN models, so no further details are described in this article.…”

Section: Local Linear Regression (Llr) and Artificial Neural Networkmentioning

confidence: 99%

Input data selection for solar radiation estimation

Ahmadi

Han

Karamouz

et al. 2009

Hydrological Processes

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Abstract:Model input data selection is a complicated process, especially for non-linear dynamic systems. The questions on which inputs should be used and how long the training data should be for model development have been hard to solve in practice. Despite the importance of this subject, there have been insufficient reports in the published literature about inter-comparison between different model input data selection techniques. In this study, several methods (i.e. the Gamma test, entropy theory, AIC (Akaike's information criterion)/BIC (Bayesian information criterion) have been explored with the aid of non-linear models of LLR (local linear regression) and ANN (artificial neural networks). The methodology is tested in estimation of solar radiation in the Brue Catchment of England. It has been found that the conventional model selection tools such as AIC/BIC failed to demonstrate their functionality. Although the entropy theory is quite powerful and efficient to compute, it failed to pick up the best input combinations. On the other hand, it is very encouraging to find that the new Gamma test was able to choose the best input selection. However, it is surprising to note that the Gamma test significantly underestimated the required training data while the entropy theory did a better job in this aspect. This is the first study to compare the performance of those techniques for model input selections and still there are many unsolved puzzles.

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“…However, in recent decades, several computational techniques and data-based statistical techniques have been successfully applied in the field of evaporation modeling using models such as ANN, Fuzzy, Neuro-fuzzy, SVM, and hybrid models [14,18,25,28]. Tan et al [26] and Piri et al [21] used ANN models for modeling daily pan/open water evaporation in dry countries. Kisi and Cimen [15] used SVM for evapotranspiration modeling, utilizing several meteorological data from three stations (Windsor, Oakville, and Santa Rosa) in central California, USA.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Evapotranspiration Modeling

Remesan

Mathew

2014

Hydrological Data Driven Modelling

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This chapter focuses on data-based modeling of evapotranspiration and evaporation from three totally different eco-climatic regions. In the first few sections, data-based modeling (artificial neural network) results are compared with reference to evapotranspiration (ET 0 ), estimated using traditional models from meteorological data. The second section is fully dedicated to evaporation modeling with data-based modeling concepts and input section procedures applied to evaporation modeling. In Sect. 7.1, we describe the mathematical details of the reference evapotranspiration models used. Analyses with traditional reference evapotranspiration models are performed on data from the Brue catchment, UK and the Santa Monica Station, USA. In Sect. 7.2, studies are described which have been conducted to see how data selection approaches respond to the evaporation data from the Chahnimeh reservoirs region in Iran. In this case study, we consider comprehensive use of data selection approaches and machine learning AI approaches. We have employed different model selection approaches such as GT, AIC, BIC, entropy theory (ET), and traditional approaches such as data splitting and cross correlation method on this daily evaporation data. Modeling with conventional models and hybrid wavelet based models was performed as per recommendations.

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Daily Pan Evaporation Modeling in a Hot and Dry Climate

Cited by 104 publications

References 53 publications

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Input data selection for solar radiation estimation

Data-Based Evapotranspiration Modeling

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