Predicting Daily Pore Water Pressure in Embankment Dam Using Machine Learning Models and Hydrostatic Seasonal Time Approach

Bilali, Ali El; Moukhliss, Mohammed; Taleb, Abdeslam; Nafii, Ayoub; Alabjah, Bahija; Brouzyine, Youssef; Mazigh, Nouhaila; Teznine, Khalid; Mhamed, Madark

doi:10.21203/rs.3.rs-947164/v1

Cited by 3 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Groundwater depletion is a significant problem in Morocco and is of particular concern to water managers. Rapid declines in groundwater levels (0.5 to 2 m per year on average) is generally caused by low groundwater recharge, marine intrusion, and excessive expansion of agricultural activities (Fadili et al, 2015;Najib et al, 2016;Ait Kadi and Ziyad, 2018;Alabjah et al, 2018;Mountadar et al, 2018;Bilali et al, 2021;Moukhliss et al, 2021;Zeynolabedin et al, 2021). Studied areas represent typical cases and are strongly impacted by climate variability, anthropogenic activity, and marine intrusion.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Quality Forecasting Using Machine Learning Algorithms: Case Study Berrechid Aquifer, Central Morocco

MOUKHLISS¹,

TALEB²,

SOUABI³

et al. 2022

AgricultForest

View full text Add to dashboard Cite

In order to provide a recommendation on the quality of groundwater in the region of Berrechid, Morocco, we analysed the concentration of conductivity as one of the main measures to identify the salinity of the water. We applied artificial intelligence models for predicting the conductivity of water while analysing several physical parameters as input parameters of the models. To achieve this purpose, we exploited and evaluated the Random Forest (RF), Support Vector Regression (SVR), and k-nearest neighbour models using 400 data samples related to ten groundwater quality parameters in the Berrechid aquifer, Morocco. The results revealed that the overall prediction performance of the RF models is higher than the SVR and KNN models. Overall, the developed models are able to predict conductivity with high accuracy. The approaches developed in this study are promising for real-time and low-cost prediction of groundwater quality by using physical parameters as input variables.

show abstract

Section: Introductionmentioning

confidence: 99%

Groundwater Quality Forecasting Using Machine Learning Algorithms: Case Study Berrechid Aquifer, Central Morocco

MOUKHLISS¹,

TALEB²,

SOUABI³

et al. 2022

AgricultForest

View full text Add to dashboard Cite

show abstract

Comparative analysis of machine learning techniques for estimating groundwater deuterium and oxygen-18 isotopes

Cemek

Arslan

Küçüktopcu

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Isotope techniques are most frequently used when hydrochemical analysis are insu cient to determine the origin and quality of groundwater and reveal seawater intrusion into groundwater along coastlines. In this study, the potential of the multilayer perceptron, adaptive neuro-fuzzy inference system, generalized regression neural networks, radial basis neural networks, classi cation and regression tree, Gaussian process regression, multiple linear regression analysis, and support vector machines were compared using known hydrochemical properties of waters for estimating deuterium (δD) and oxygen-18 (δ 18 O) isotopes in groundwater of the Bafra plain, Northern Turkey. The data were divided into training (70%) and testing (30%) sets. Cluster analysis was performed to decrease the number of input variables. The data on electrical conductivity, chloride, magnesium, and sulfate were introduced into the models after examining different combinations of these variables in the studied models. The determination coe cient (R 2 ), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the performances of the models. In addition, visualization techniques (Taylor diagram and heat maps) were prepared to assess the similarities between the measured and estimated δD and δ 18 O values. The R 2 , RMSE, and MAE for δ 18 O (0.98, 0.31‰, and 0.20‰, respectively), and δD (0.95, 2.85‰, and 1.89‰, respectively) values for the testing datasets revealed that the performance accuracy of multilayer perceptron is the best among the applied models tested. Therefore, the study suggests using data-driven methods, multilayer perceptron in this case, when lacking appropriate laboratory isotope analysis or facing high laboratory analysis costs.

show abstract

Predicting Daily Suspended Sediment Load Using Machine Learning and NARX Hydro-Climatic Inputs in Semi-Arid Environment

Ezzaouini

Mahé

Kacimi

et al. 2022

Water

View full text Add to dashboard Cite

Sediment transport in basins disturbs the ecological systems of the water bodies and leads to reservoir siltation. Its evaluation is crucial for managing water resources. The practical application of the process-based model can confront some limitations noticed in the lower accuracy during the validation process due to the lack of reliable physical datasets. In this study, we attempt to apply machine-learning-based modeling (ML) to predict the suspended sediment load, using hydro-climatic data as input variables in the semi-arid Bouregreg basin, Morocco. To that end, data for the years 2016 to 2020 were used for the training process, and the validation was performed with 2021 data. The results showed that most ML models have good accuracy, with a Nash–Schiff efficiency (NSE) ranging from 0.47 to 0.80 during the validation phase, which indicates satisfactory performances in predicting the SSL. Furthermore, the models were ranked against their generalization ability (GA), which revealed that the developed models are good to excellent in terms of GA. Overall, the present study provides new insight into predicting the SSL in a semi-arid environment, such as the Bouregreg basin.

show abstract

Predicting Daily Pore Water Pressure in Embankment Dam Using Machine Learning Models and Hydrostatic Seasonal Time Approach

Cited by 3 publications

References 43 publications

Groundwater Quality Forecasting Using Machine Learning Algorithms: Case Study Berrechid Aquifer, Central Morocco

Groundwater Quality Forecasting Using Machine Learning Algorithms: Case Study Berrechid Aquifer, Central Morocco

Comparative analysis of machine learning techniques for estimating groundwater deuterium and oxygen-18 isotopes

Predicting Daily Suspended Sediment Load Using Machine Learning and NARX Hydro-Climatic Inputs in Semi-Arid Environment

Contact Info

Product

Resources

About