Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model

Malik, Anurag; Rai, Priya; Heddam, Salim; Kisi, Özgür; Sharafati, Ahmad; Salih, Sinan Q.; Al‐Ansari, Nadhir; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.3390/atmos11060553

Cited by 39 publications

(20 citation statements)

References 78 publications

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“…Many researchers have conducted research with neural networks aimed at the estimation of Ep as a function of meteorological variables (Keskin and Terzi, 2006). Several of these researchers found better results in Ep estimation with neural network than those obtained from the Priestley-Taylor and the Penman methods (Rahimikhoob, 2009;Malik et al, 2020). Consistent with other studies, this study demonstrated that modelling of Ep is possible through the use of K-SOM technique in addition to the FAO56-PM and MLR methods.…”

Section: Discussionsupporting

confidence: 84%

Estimation Standard And Seeded Pan Evaporation Using Modelling Approach

Simon-Gáspár

Soós

Anda

2021

Preprint

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Abstract. Evaporation is an important meteorological variable that has also a great impact on water management. In this study, FAO-56 Penman-Monteith equation (FAO56-PM), multiple stepwise regression (MLR) and Kohonen self-organizing map (K-SOM) techniques were used for the estimation of daily pan evaporation (Ep) in three treatments, where C was the standard class A pan with top water, S was A pan with sediment covered bottom, and SM was class A pan containing submerged macrophytes (Myriophyllum sipctatum., Potamogeton perfoliatus, and Najas marina), in an six-season experiment. The modelling approach included six measured meteorological variables; daily mean air temperatures (Ta), maximum and minimum air temperatures, global radiation (Rs), relative humidity (RH), and wind speed (u) in the 2015–2020 growing seasons (from June to September), at Keszthely, Hungary. Average Ep varied from 0.6 to 6.9 mm d−1 for C, 0.7 to 7.9 mm d−1 for S, whereas from 0.9 to 8.2 mm d−1 for SM during the growing seasons studied. Correlation analysis and K-SOM visual representation revealed that Ta and Rs had stronger positive correlation, while RH had a negative correlation with the Ep of C, S and SM. Performances of the different models were compared using statistical indices, which included the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and Nash-Sutcliffe efficiency (NSE). The results showed that the MLR method provided close compliance with the observed pan evaporation values, but the K-SOM method gave better estimates than the other methods. Overall, K-SOM has high accuracy and huge potential for Ep estimation for water bodies where freshwater submerged macrophytes are present.

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Section: Discussionsupporting

confidence: 84%

Estimation Standard And Seeded Pan Evaporation Using Modelling Approach

Simon-Gáspár

Soós

Anda

2021

Preprint

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“…Pearson's correlation coefficient: CC shows the interrelation between observed and predicted values and ranges from -1 to þ1. It can be calculated as (Malik et al 2019b(Malik et al , 2020a):…”

Section: Performance Evaluation Indicatorsmentioning

confidence: 99%

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

et al. 2021

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In this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE20) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE20 at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 at Parshall and Modified Venturi flumes.

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“…Predicting evaporation is an essential issue for monitoring water resources. In arid regions, evaporation prediction is vital for decision-makers because of water shortage (Malik et al, 2020a;Seifi and Riahi, 2020). Direct and indirect methods are used for predicting evaporation.…”

Section: Introductionmentioning

confidence: 99%

Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

Ehteram

Panahi

Ahmed

et al. 2022

Front. Environ. Sci.

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Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANN-CSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112 mm/day, while it was 0.189 mm/day, 0.267 mm/day, 0.267 mm/day, 0.389 mm/day, 0.456 mm/day, and 0.512 mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation.

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Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model

Cited by 39 publications

References 78 publications

Estimation Standard And Seeded Pan Evaporation Using Modelling Approach

Estimation Standard And Seeded Pan Evaporation Using Modelling Approach

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

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