A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models

Shirsath, Paresh B.; Singh, Anil Kumar

doi:10.1007/s11269-009-9514-2

Cited by 99 publications

(40 citation statements)

References 21 publications

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“…Figure 4 summaries the efficiency of the RPD model in predicting the observed rainfall events during various time periods. From Figure 4, it propagation neural networks, consists of three distinct layers i) input layer, ii) hidden layer & iii) output layer [14][15][16]. A network consists one input, one or more hidden layers and one output layer.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Decision Tree Based Rainfall Prediction Model with Data Driven Model Considering Climatic Variables

Ramsundram¹,

Sathya²,

Karthikeyan³

2016

Irrigat Drainage Sys Eng

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Section: Resultsmentioning

confidence: 99%

Comparison of Decision Tree Based Rainfall Prediction Model with Data Driven Model Considering Climatic Variables

Ramsundram¹,

Sathya²,

Karthikeyan³

2016

Irrigat Drainage Sys Eng

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“…Given the good accuracy of soft computing models, the majority of studies in this field have used these models for predicting the daily pan evaporation. In a study by Shirsath and Singh [17], the ANN and regression based models were used to estimate daily pan evaporation and compared with the multiple linear regression. In a study by Chang et al [18], the self-organizing map neural network and back propagation neural network were compared in estimating daily pan evaporation.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

Kişi

Mansouri

2017

Advances in Meteorology

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Evaporation estimation is very essential for planning and development of water resources. The study investigates the ability of new method, dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. Monthly maximum and minimum temperatures, solar radiation, wind speed, and relative humidity data obtained from two stations located in Turkey are used as inputs to the models. The results of DENFIS method were compared with the classical adaptive neural-fuzzy inference system (ANFIS) by using root mean square error (RMSE), mean absolute relative error (MARE), and Nash-Sutcliffe Coefficient (NS) statistics. Cross validation was applied for better comparison of the models. The results indicated that DENFIS models increased the accuracy of ANFIS models to some extent. RMSE, MARE, and NS of the ANFIS model were increased by 11.13, 11.45, and 6.83% for the Antalya station and 20.11, 12.94%, and 8.29% for the Antakya station using DENFIS.

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“…They found that the SVM and ANFIS had better performance in ET modeling compared to the others. Besides, several studies have shown high performance of ANNs in modeling ET against statistical methods [8,[25][26][27][28][29]. The substantial benefit of ANN methods, compared to conventional methods, is the ability of solving problems that are difficult to formalize [30].…”

Section: Introductionmentioning

confidence: 99%

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

Alipour

Yarahmadi

Mahdavi

2014

Journal of Climatology

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Reference evapotranspiration (ET ) 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 ET 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 ET values and their results were compared with calculated ET 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 ET . 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 (2 ) values for comparison between calculated ET by FAO-PM and estimated ET by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, 2 values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate ET by means of LST data derived from MODIS sensor.

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A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models

Cited by 99 publications

References 21 publications

Comparison of Decision Tree Based Rainfall Prediction Model with Data Driven Model Considering Climatic Variables

Comparison of Decision Tree Based Rainfall Prediction Model with Data Driven Model Considering Climatic Variables

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

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

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