Journal of Fundamentals of Renewable Energy and Applications
Open Access
ISSN: 2090-4541
+44 1300 500008
ISSN: 2090-4541
+44 1300 500008
Othman Fallatah
King Abdul Aziz University Kingdom of Saudi Arabia, UAE
Scientific Tracks Abstracts: J Fundam Renewable Energy Appl
In this study, we developed and used an automated machine learning (AML) approach to quantify relationships between gross α and gross β activities and different geological, hydrogeological, and geochemical conditions. Two AML model groups (group I for gross α; group II for gross β) were constructed, using water samples collected from 360 irrigation and water supply wells, to define a robust model that explains the spatial variability in gross α and gross β activities, as well as variables that control the gross activities. Each group contained four model families: deep neural network (DNN), gradient boosting machine (GBM), generalized linear model (GLM), and distributed random forest (DRF). Model inputs include chemical compositions as well as geological and hydrogeological conditions. Three performance metrics were used to evaluate the models during training and testing: normalized root mean square error (NRMSE), Pearson’s correlation coefficient (r), and Nash-Sutcliff efficiency (NSE) coefficient. Results indicate that (1) the GBM model out performed (training: NRMSE: 0.37 ± 0.10; r: 0.92 ± 0.05;NSE: 0.85 ± 0.09; testing: NRMSE: 0.71 ± 0.08; r: 0.72 ±0.08; NSE: 0.49 ± 0.12) the DNN, DRF, and GLM models when modelling gross α activities; (2) gross α activities are controlled by pH, stream density, nitrate, manganese, and vegetation index; (3) the DRF model outperformed (training: NRMSE: 0.41 ± 0.05; r: 0.92 ± 0.02; NSE: 0.83 ± 0.04; testing: NRMSE: 0.67 ± 0.09; r: 0.77 ± 0.07; NSE: 0.54 ±0.12) the GBM, DNN, and GLM models when modelling gross β activities; (4) input variables that affect the gross βactives are pH, temperature, stream density, lithology, and nitrate; and (5) no single model could be used to model both gross α and gross β activities—instead, a combination of AML models should be us In this study, we developed and used an automated machine learning (AML) approach to quantify relationships between gross α and gross β activities and different geological, Hydro geological, and geochemical conditions. Two AML model groups (group I for gross α; group II for gross β) were constructed, using water samples collected from 360 irrigation and water supply wells, to define a robust model that explains the spatial variability in gross α and gross β activities, as well as variables that control the gross activities. Each group contained four model families: deep neural network (DNN), gradient boosting machine (GBM), generalized linear model (GLM), and distributed random forest (DRF). Model inputs include chemical compositions as well as geological and hydrogeological conditions. Three performance metrics were used to evaluate the models during training and testing: normalized root mean square error (NRMSE), Pearson’s correlation coefficient (r), and Nash-Sutcliff efficiency (NSE)coefficient. Results indicate that (1) the GBM model out performed (training: NRMSE: 0.37 ± 0.10; r: 0.92 ± 0.05;NSE: 0.85 ± 0.09; testing: NRMSE: 0.71 ± 0.08; r: 0.72 ±0.08; NSE: 0.49 ± 0.12) the DNN, DRF, and GLM models when modelling gross α activities; (2) gross α activities are controlled by pH, stream density, nitrate, manganese, and vegetation index; (3) the DRF model outperformed (training :NRMSE: 0.41 ± 0.05; r: 0.92 ± 0.02; NSE: 0.83 ± 0.04;testing: NRMSE: 0.67 ± 0.09; r: 0.77 ± 0.07; NSE:0.54 ±0.12) the GBM, DNN, and GLM models when modelling gross β activities; (4)input variables that affect the gross βactives are pH, temperature, stream density, lithology, and nitrate; and (5) no single model could be used to model both gross α and gross β activities—instead, a combination of AML models should be used.ed.
Othman. A. Fallatah . Assistance Professor, Faculty of Nuclear Engineering, and Radiation Protection & Training Centre, King Abdulaziz University, Saudi Arabia, Jeddah. He received his Ph.D. in Environmental Engineering from the University of Rhode Island, USA. His research interested in ( Environmental Radioactivity, GIS, Remote Sensing, hydrology, modeling). As part of his current research activities, he builds upon the chemistry background he acquired during his B.Sc., which involved monitoring and analyzing water quality problems in KSA and natural radioactive pollution, as well as understanding geology, hydrology,andgeochemistry.