The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt

Elbeltagi, Ahmed; Aslam, Muhammad; Malik, Anurag; Mehdinejadiani, Behrouz; Srivastava, Ankur; Bhatia, Amandeep Singh; Deng, Jinsong

doi:10.1016/j.scitotenv.2020.140770

Cited by 93 publications

(24 citation statements)

References 51 publications

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“…Artificial neural network (ANN) is a system that inspired its dynamic functionality from the simulation of human nervous system. It was used for the first time by McCulloch and Pitts (1943), where the method works to create a relationship between inputs and outputs through assigned weights which plays the role of a mathematical memory (Elbeltagi et al 2020c).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

et al. 2021

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Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, and 3.1708*10–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

et al. 2021

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“…Maize is one of the important crops in Egypt (Fahmi and Abbas, 2012;Elbeltagi et al, 2020). Its production covers approximately 55% of the local marker and the gap between production and consumption is compensated by importation (Noreldin et al, 2016).…”

Section: Effect Of Traditional Sources Of Zn and Zno-nano-particles Foliar Application On Productivity And P-uptake Of Maize Plants Grownmentioning

confidence: 99%

Effect of traditional sources of Zn and ZnO-nano-particles foliar application on productivity and P-uptake of maize plants grown on sandy and clay loam soils

Noufal

Farid

Attia³

et al. 2021

EBSS

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A PPLYING Zn as soil fertilizers might undergo precipitation in presence of phosphate fertilizers; alternatively, this nutrient may be amended through foliar application. Specifically, we hypothesized that the effect of Zn foliar application is quite enough to overcome Zn deficiency beside its synergetic effect on P uptake. To test this hypothesis, a field study was conducted in a split-plot design where Zn-sources, i.e. ZnSO 4 , Zn-EDTA and ZnO-nano-particles (ZnO-NP) were plotted in main plots while their rates of application (0, 5, 10 and 15 mg L -1 ) were schemed in sub-plots. Results obtained herein indicate that these three fertilizers not only improved significantly concentrations of both P and Zn in maize during V14-fouteenth leaf and physiological maturity growth stages but also increased significantly the available contents of these nutrients in soil, especially with increasing the level of foliar application. Moreover, these fertilizers improved significantly plant height during the abovementioned growth stages and raised grain yield and 100-grain weight. The efficiency of these fertilizers could be arranged as follows: ZnO-NP> Zn-EDTA> ZnSO 4 . In conclusion, Zn foliar application especially in the nano form recorded a synergic effect on P availability and uptake; consequently these applications increased maize production as just mentioned with superiority for ZnO-NP; however, further studies are needed to investigate the residual effects of these nano-particles within the edible plant parts.

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“…In another carried out by Adamala and Srivastava, 2018 it was found that the kernel based methods are one of the best alternatives to understand the rainfall-runoff relationship in the catchment. In particular to this climatology, the discharge estimation under predicts the observed streamflows which may due to the to the overprediction of model simulated water balance losses, such as base flow and ET (Paul et al, 2018;Srivastava et al, 2020;Elbeltagi et al, 2020).…”

Section: Evaluation Of Different Models Using Statistical Indicatorsmentioning

confidence: 99%

Prediction of Daily Streamflow Using Various Kernel Function Based Regression: A Case Study in India

Kumar

Elbeltagi

Srivastava

et al. 2021

Preprint

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River daily discharge estimation and modeling considers an important step for scheduling and planning different water resources for sustainable socio-economic development. In the current work, four techniques of Gaussian processes regression (GPR): Polynomial Kernel, Radial Basis Function Kernel, Normalized Polynomial Kernel, and PUK Kernel, were used to model the daily discharge. Hydrological-datasets containing daily-stage (m) and discharge (m3/sec) were gathered over the period from 2004-2013. The datasets were divided into two sections: (i) models training containing 70% (2004-2010) of the total data and (ii) remaining 30% (2011- 2013) were for testing. Comparing all the four developed models, our findings show that the superlative model was the PUK-Kernel model with a correlation coefficient (r) of 0.96, MAE of 36.70 m3/s, RMSE of 90.92 m3/s, RAE of 17.50 %, RRSE of 26.05 % in the training period. Whereas, it performed equally well in the testing period with r = 0.97, MAE = 44.84 m3/s, RMSE = 95.05 m3/s, RAE = 17.98 %, RRSE = 24.94 % in the testing period. Our findings can be included that GPR-PUK was more accurate and stable than other models, and can be used to help water-users, decision-makers, development-planners for managing water resources and achieving sustainable development.

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The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt

Cited by 93 publications

References 51 publications

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Effect of traditional sources of Zn and ZnO-nano-particles foliar application on productivity and P-uptake of maize plants grown on sandy and clay loam soils

Prediction of Daily Streamflow Using Various Kernel Function Based Regression: A Case Study in India

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