Elaboration of an innovative plant biomaterial for its valorization in the treatment of wastewater.

The global scientific community is deeply concerned about the deterioration of water quality resulting from the release of industrial effluents. This issue is of utmost importance as it serves to safeguard the environment and combat water pollution. The objective of this work is to elaborate a biomaterial of vegetable origin, based on the twigs of Aleppo pine, and to use it as an abundant and less expensive material for the treatment of wastewater. For this reason, the twigs were treated physically to get the powder called biomaterial FPA (Aleppo pine fiber), which was characterized by physicochemical, and spectroscopic analyses namely scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The crystallinity index of FPA was evaluated by the peak height method. The findings indicate that the FPA powder has an acidic nature, exhibiting a porous structure that promotes the adsorption and binding of molecules. Additionally, it has a zero charge point of 5.8 and a specific surface area of 384 m2.g-1. It is primarily composed of hydroxyl, carboxyl, and amine functional groups, along with mineral compounds and organic compounds, including cellulose and other mineral elements such as Ca, Mg, Fe, Na, P, Al, K, Ni, and Mo. Combining these characteristics, FPA biomaterial has considerable potential for use as an effective adsorbent biomaterial for various wastewater pollutants. Its abundance and relatively low cost make it an attractive solution to the growing challenges of water pollution worldwide.

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment.

Machine Learning models have become a fruitful tool in water resources modelling. However, it requires a significant amount of datasets for training and validation, which poses challenges in the analysis of data scarce environments, particularly for poorly monitored basins. In such scenarios, using Virtual Sample Generation (VSG) method is valuable to overcome this challenge in developing ML models. The main aim of this manuscript is to introduce a novel VSG based on multivariate distribution and Gaussian Copula called MVD-VSG whereby appropriate virtual combinations of groundwater quality parameters can be generated to train Deep Neural Network (DNN) for predicting Entropy Weighted Water Quality Index (EWQI) of aquifers even with small datasets. The MVD-VSG is original and was validated for its initial application using sufficient observed datasets collected from two aquifers. The validation results showed that from only 20 original samples, the MVD-VSG provided enough accuracy to predict EWQI with an NSE of 0.87. However the companion publication of this Method paper is El Bilali et al. [1]. •Development of MVD-VSG to generate virtual combinations of groundwater parameters in data scarce environment.•Training deep neural network to predict groundwater quality.•Validation of the method with sufficient observed datasets and sensitivity analysis.

Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling.

Dam safety assessment is important to implement the appropriate measures to avoid a dam break disaster as part of the water reservoirs management process. Prediction-based approaches are valuable to compare the actual measurements with the simulated values to proactively detect anomalies. However, the application of the conventional hydrostatic seasonal time (HST) has some limitations related to an instantaneous response of the dam to environmental factors, which can lead to inaccurate prediction and interpretation, especially for daily measurements. Besides, the generalization ability (GA) of these models is not analyzed enough despite its crucial importance in selecting the appropriate models. In this study, the multiple linear regression (MLR), artificial neural network (ANN), support vector regression (SVR), and adaptive boosting (AdaBoost) models with nonlinear autoregressive exogenous (NARX) inputs are proposed to incorporate the response delay of the dam to the hydraulic load. Thus, these models were evaluated and compared with the HST model for predicting the daily pore water pressure in an embankment dam. Moreover, we proposed a classification method of the models into four categories, namely perfect, excellent, good, and poor according to the GA. Results show that, except for the AdaBoost, the other ML models outperformed the traditional statistical approach (HST) in terms of prediction accuracy as well as the GA. Overall, the study results provide new insights in enhancing the monitoring processes and dam safeties by detecting the anomalies early through the comparison of the measurements and simulated results produced by the best-fitted models from the confidence interval (CI) perspective.