| 1. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
- 2024
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Ingår i: Potato Research. - : Springer Nature. - 0014-3065 .- 1871-4528.
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Tidskriftsartikel (refereegranskat)abstract
- Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.
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| 2. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Winter Potato Water Footprint Response to Climate Change in Egypt
- 2022
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Ingår i: Atmosphere. - : MDPI. - 2073-4433. ; 13:7
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Tidskriftsartikel (refereegranskat)abstract
- The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity.
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| 3. |
- Abd El‑Hameed, Mona M., et al.
(författare)
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Phycoremediation of contaminated water by cadmium (Cd) using two cyanobacterial strains (Trichormus variabilis and Nostoc muscorum)
- 2021
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Ingår i: Environmental Sciences Europe. - Germany : Springer Nature. - 2190-4707 .- 2190-4715. ; 33:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundWater pollution with heavy metals is a severe dilemma that concerns the whole world related to its risk to natural ecosystems and human health. The main objective was to evaluate the removal efficiency of Cd of various concentrations from contaminated aqueous solution by use of two cyanobacterial strains (Nostoc muscorum and Trichormus variabilis). For this purpose, a specially designed laboratory pilot-scale experiment was conducted using these two cyanobacterial strains on four different initial concentrations of Cd (0, 0.5, 1.0 and 2.0 mg L−1) for 21 days.ResultsN. muscorum was more efficient than T. variabilis for removing Cd (II), with the optimum value of residual Cd of 0.033 mg L−1 achieved by N. muscorum after 21 days with initial concentration of 0.5 mg L−1, translating to removal efficiency of 93.4%, while the residual Cd (II) achieved by T. variabilis under the same conditions was 0.054 mg L−1 (89.13% removal efficiency). Algal growth parameters and photosynthetic pigments were estimated for both cyanobacterial strains throughout the incubation period.ConclusionsHigh Cd concentration had a more toxic impact on algal growth. The outcomes of this study will help to produce treated water that could be reused in agrarian activities.
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| 4. |
- Mokhtar, Ali, et al.
(författare)
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Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region
- 2023
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Ingår i: Water resources management. - : Springer. - 0920-4741 .- 1573-1650. ; 37, s. 1557-1580
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Tidskriftsartikel (refereegranskat)abstract
- Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions.
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| 5. |
- Mokhtar, Ali, et al.
(författare)
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Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield
- 2022
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Ingår i: Frontiers in Plant Science. - : Frontiers Media S.A.. - 1664-462X. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield.
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