Prediction of sodium adsorption ratio and chloride concentration in a coastal aquifer under seawater intrusion using machine learning models

Bilali, Ali El; Taleb, Abdeslam; Nafii, Ayoub; Alabjah, Bahija; Mazigh, Nouhaila

doi:10.1016/j.eti.2021.101641

Cited by 26 publications

(6 citation statements)

References 33 publications

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“…It is based on a hyper-plane in order to minimize the error and the kernel function, such as radial basis function [43], sigmoid, linear kernel, and polynomial function. This method has demonstrated a high accuracy prediction for several regression applications [24].…”

Section: Adaboostmentioning

confidence: 97%

“…In Morocco, water resource planning and management processes are facing several issues, such as reservoir sedimentation, the continuous decline of groundwater level in most aquifers, seawater intrusion into the costal aquifers, inappropriate practices applied to groundwater-based agriculture, and flood risk [24][25][26]. Importantly, according to the Water Department of Morocco, reservoir silting causes a global decrease in the reservoir capacity of approximately 70 mm 3 •yr −1 ,which means 0.4% each year, with large regional variability, and much higher values in some regions, such as the north of the country.…”

Section: Introductionmentioning

confidence: 99%

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

Ezzaouini

Mahé

Kacimi

et al. 2022

Water

Self Cite

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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.

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Section: Adaboostmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Ezzaouini

Mahé

Kacimi

et al. 2022

Water

Self Cite

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“…L. Wang et al [ 13 ] and J. Liu et al [ 14 ] proposed M-DJINN and wavelet transform-depth Bi–S-SRU prediction models, respectively, and the proposed models achieved 96% prediction accuracy. A. Bilali et al [ 15 ] developed stochastic gradient descent for linear regression (SGD), an artificial neural network (ANN), k-nearest neighbors (k-NN), and support vector machine (SVM) prediction models for chloride concentration and the sodium adsorption ratio using physical parameters as inputs to the models, and SGD and ANN outperformed the other methods in terms of R 2 and RMSE. J. Jiang et al [ 16 ] constructed a support vector machine (SVR), deep neural network (DNN), and RandomForest, ElasticNet, and XGBoost prediction models to determine the abundance of Vibrio vulnificus in estuarine and mariculture areas, with input parameters of temperature, salinity, dissolved oxygen, pH, total nitrogen, and total phosphorus, and used SHapley Additive exPlanations (SHAP) to analyze salinity and temperature as the main influential factors of Vibrio abundance, proving that DNN and RandomForest outperformed the other methods in two evaluation indexes, RMSE and MAE.…”

Section: Introductionmentioning

confidence: 99%

A PCA–EEMD–CNN–Attention–GRU–Encoder–Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay

Xie

et al. 2022

Materials

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In order to effectively solve the problem of low accuracy of seawater water quality prediction, an optimized water quality parameter prediction model is constructed in this paper. The model first screened the key factors of water quality data with the principal component analysis (PCA) algorithm, then realized the de-noising of the key factors of water quality data with an ensemble empirical mode decomposition (EEMD) algorithm, and the data were input into the two-dimensional convolutional neural network (2D-CNN) module to extract features, which were used for training and learning by attention, gated recurrent unit, and an encoder–decoder (attention–GRU–encoder–decoder, attention–GED) integrated module. The trained prediction model was used to predict the content of key parameters of water quality. In this paper, the water quality data of six typical online monitoring stations from 2017 to 2021 were used to verify the proposed model. The experimental results show that, based on short-term series prediction, the root mean square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R2 were 0.878, 0.594, and 92.23%, respectively, which were all better than the prediction model based on an enhanced clustering algorithm and adam with a radial basis function neural network (ECA–Adam–RBFNN), a prediction model based on a softplus extreme learning machine method with partial least squares and particle swarm optimization (PSO–SELM–PLS), and a wavelet transform-depth Bi–S–SRU (Bi-directional Stacked Simple Recurrent Unit) prediction model. The PCA–EEMD–CNN–attention–GED prediction model not only has high prediction accuracy but can also provide a decision-making basis for the water quality control and management of aquaculture in the waters around Zhanjiang Bay.

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“…Researchers in recent years have used several classical algorithms to estimate and predict WQPs (Olyaie et al 2017). Bilali et al (2021) employed different ML algorithms for WQPs prediction. This study showed that the stochastic gradient descent (SGD) and artificial neural network (ANN) had better accuracy than other algorithms.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Kadkhodazadeh

Farzin

2022

Journal of Water and Climate Change

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Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For the first time, a novel hybrid framework, namely the adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), for estimation of electrical conductivity (EC) and total dissolved solids (TDS) is used. Before estimating WQPs, the performance of the ANFIS–DWT–GBO is proven by several benchmark data sets. In addition, three benchmark algorithms, including ANFIS, ANFIS–DWT, and ANFIS–GBO, are used to demonstrate the strength of the novel framework. The principal component analysis (PCA) method determines the best input combination in EC and TDS estimation. The consequences show that the ANFIS–DWT–GBO produces very successful and competitive results in benchmark data sets modeling and WQPs estimation compared to other algorithms. This result is due to the simultaneous use of DWT and optimization algorithm in the proposed framework. DWT can process WQP data before applying it to the algorithms. The GBO is utilized to optimize the hyperfine parameters in the ANFIS. The results show that the highest accuracy of estimating EC and TDS is in Mollasani and Gotvand stations, respectively. The correlation coefficient (R) value in the Mollasani station is 0.99, and in the Gotvand station is 0.98.

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Prediction of sodium adsorption ratio and chloride concentration in a coastal aquifer under seawater intrusion using machine learning models

Cited by 26 publications

References 33 publications

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

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

A PCA–EEMD–CNN–Attention–GRU–Encoder–Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

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