Spatial and temporal variability analysis of green and blue evapotranspiration of wheat in the Egyptian Nile Delta from 1997 to 2017

Elbeltagi, Ahmed; Aslam, Muhammad; Mokhtar, Ali; Deb, Proloy; Abubakar, Ghali Abdullahi; Kushwaha, Nand Lal; Venâncio, Luan Peroni; Malik, Anurag; Kumar, Navsal; Deng, Jinsong

doi:10.1016/j.jhydrol.2020.125662

Cited by 40 publications

(32 citation statements)

References 40 publications

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“…The values for RMSE, MAPE, NRMSE were found to be the lowest at 0.008 mm, 0.000%, and 0.000%, respectively, of all the combinations, whereas values for ACC, and R 2 of 0.999%, and 0.99, respectively, were the highest of all the combinations. The next best result was obtained for the C4 (6, 4) followed by C7 (3, 2), C8 (5, 4), C1 (5, 4), and C6 (6,4). The results show the combination C3 had the worst performance followed by C5.…”

Section: Data Fusion Of Climatic Factors For Modelling the Et Omentioning

confidence: 90%

“…The values of RMSE (mm), MAPE (%), NRMSE (%), ACC (%) and R 2 for the C3 combination (Grad max + Sun shine hours + Windspeed) did not vary too much and were obtained in the ranges of 0.548-0.549, 0.315-0.318, 1.943-2.038, 0.994-0.996, and 0.30-0.31, respectively. Further, the combination C4 (Grad min + Solar Radiation +Windspeed) showed good results in the hidden layer (6,4) of the ANN model with the lowest values of RMSE, MAPE, NRMSE as 0.010 (mm), 0.000 (%), and 0.0001 (%) and highest values of ACC and R 2 as 0.999% and 0.99.…”

Section: Data Fusion Of Climatic Factors For Modelling the Et Omentioning

confidence: 93%

“…The process has an overall error of about 0.003. Model B had hidden neuron layers (6,4) and the training process needed 5288 steps with an overall error of about 0.001. Furthermore, in Model C with hidden neuron layers (3, 2), the training process needed 55,640 steps until all absolute partial derivatives of the error function were smaller than the default threshold of 0.01, with the process having an overall error of about 0.004.…”

Section: Structural Network Design Of the Superior Modelsmentioning

confidence: 99%

“…The fundamental goal of irrigation is to apply water to maintain crop evapotranspiration when rainfall is insufficient [2,3]. Evapotranspiration (ET) is a connection between the energy and water cycles, and climate is the primary source of water vapor in the atmosphere [4][5][6][7]. It regulates the quantity of water needed for vegetation development, surface runoff, water loss from water bodies, and water requirements for ecological sustainability.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

et al. 2022

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Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ETo in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners.

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Section: Data Fusion Of Climatic Factors For Modelling the Et Omentioning

confidence: 90%

Section: Data Fusion Of Climatic Factors For Modelling the Et Omentioning

confidence: 93%

Section: Structural Network Design Of the Superior Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

et al. 2022

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“…Daily pan evaporation (EP d ) has been extensively used in irrigation scheduling, water balance studies, sustainable water resources management, and hydrological modelling, etc. [2,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

et al. 2021

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Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

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Advances in Micro-Irrigation Practices for Improving Water Use Efficiency in Dryland Agriculture

Patel

Kushwaha

Rajput

et al. 2023

Enhancing Resilience of Dryland Agriculture Under Changing Climate

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Spatial and temporal variability analysis of green and blue evapotranspiration of wheat in the Egyptian Nile Delta from 1997 to 2017

Cited by 40 publications

References 40 publications

Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

Advances in Micro-Irrigation Practices for Improving Water Use Efficiency in Dryland Agriculture

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