Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt

Elbeltagi, Ahmed; Azad, Nasrin; Arshad, Arfan; Mohammed, Safwan; Mokhtar, Ali; Pande, Chaitanya B.; Etedali, Hadi Ramezani; Bhat, Shakeel Ahmad; Islam, Abu Reza Md. Towfiqul; Deng, Jinsong

doi:10.1016/j.agwat.2021.107052

Cited by 43 publications

(14 citation statements)

References 65 publications

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“…However, the high accuracy of the SVM model validation set was due to its better robustness, suitability for small sample data regression, and the lack of sensitivity to kernel functions with the ability to avoid dimensional catastrophe problems. [97,98]. We also found that the accuracy of yield estimation models constructed using the four independent machine learning algorithms, SVM, GP, LRR, and RF, at the two developmental stages of winter wheat also differed greatly.…”

Section: Discussionmentioning

confidence: 78%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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Section: Discussionmentioning

confidence: 78%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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“…The kernel function is used in the GPR design process. In the literature, several kernels have been discussed [34][35][36]. The following three kernel functions are used in this study:…”

Section: Details Of Kernel Functionsmentioning

confidence: 99%

Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression

et al. 2022

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In the design stage of construction projects, determining the soil permeability coefficient is one of the most important steps in assessing groundwater, infiltration, runoff, and drainage. In this study, various kernel-function-based Gaussian process regression models were developed to estimate the soil permeability coefficient, based on six input parameters such as liquid limit, plastic limit, clay content, void ratio, natural water content, and specific density. In this study, a total of 84 soil samples data reported in the literature from the detailed design-stage investigations of the Da Nang–Quang Ngai national road project in Vietnam were used for developing and validating the models. The models’ performance was evaluated and compared using statistical error indicators such as root mean square error and mean absolute error, as well as the determination coefficient and correlation coefficient. The analysis of performance measures demonstrates that the Gaussian process regression model based on Pearson universal kernel achieved comparatively better and reliable results and, thus, should be encouraged in further research.

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“…Moreover, the results of the model are extremely In addition, in simulation of the future WF based on these factors, Elbeltagi, 2020 employed a Deep Neural Networks (DNN) approach and reported that their results will assist optimize future climate change water planning for the agricultural sector (Elbeltagi et al, 2020). Moreover, the ndings coincide with Elbeltagi, 2021 results who indicated that the best Gaussian process kernel in the blue WF prediction was PUK, followed by the Poly (Elbeltagi et al, 2021b). Also, they showed that the error value of the blue WF forecasting has decreased, and the value of the correlation index has grown by increasing the numbers of meteorological variables.…”

Section: Comparison Of the Machine Learning Modelsmentioning

confidence: 74%

“…During the last decades, in the domain of water sciences and technologies the use of various machine learning technology has considerable relevance like arti cial neural networks (ANN) (Landeras et al, 2008, Antonopoulos and Antonopoulos, 2017), support vector machines (SVM) (Shiri et al, 2014), fuzzy logic models, neuro-fuzzy models, support vector machines, random forest (Elbeltagi et al), and k-Nearest Neighbor (k-NN) (Heddam, 2014, Rehman et al, 2019. Machine learning approaches were widely used to predict reference evaporation, actual of the water and to predict water resources variables, hydrological cycles, management of water resources, water quality prevision and storage activities (Elbeltagi et al, 2021b, Elbeltagi et al, 2021c. Goyal, 2014 employed ANN and other machine learning approaches such as least squares support vector regression (LS-SVR) and fussy logic to estimate regular evaporation in subtropical climates (Goyal et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimation of potato water footprint using machine leaning algorithms models in arid regions

Mohamed

Abuarab

Al‐Ansari

et al. 2023

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Precise assessment of water footprint to enhance water consumption and crop yields for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than others and is the most frequently used technique to calculate water footprint it requires a significant number of meteorological parameters at different spatio-temporal scales, sometimes inaccessible in many of the poor nations. Due to the greatest performance in the non-linear relations of inputs and output of the model, the complex hydrological phenomena are frequently described in machine learning models. Therefore, the objective of this research is to 1) develop and compare between the four-machine learning: Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB) and Artificial Neural Network (ANN) over three potato’s governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in Delta, Egypt and 2) select the best model in the best combination of climate input variables, which achieves high precision and low error in forecasting potato blue WF. The available variables for this study are 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 potato BWFP during (1990–2016). Six scenarios of input variables were used to test the weight of each variable in for four applied models. Different statistical indicators have been used to assess applied model performance (NSE, RMSE, MAE, MBE, A, R2, SI and MAPE). The results demonstrated that Sc5 with the XGB and ANN model is competent enough to evaluate BWF only if there are just 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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Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt

Cited by 43 publications

References 65 publications

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression

Estimation of potato water footprint using machine leaning algorithms models in arid regions

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