Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt

Coastal evolution and future projections in Conde County, Brazil: A multi-decadal assessment via remote sensing and sea-level rise scenarios

Global sea levels, having risen by approximately 20 cm since the mid-19th century, necessitate a critical examination of their impacts on shoreline dynamics. This research evaluates the historical (1985-2022) and future shoreline changes in Conde County, Paraíba State, Brazil, an area of significant touristic interest. Employing Landsat satellite imagery, the study utilized the Digital Shoreline Analysis System (DSAS) and a Kalman filter algorithm for cloud removal, while also assessing land use and land cover changes using data from the MapBiomas Project for 2000, 2010, and 2020. These analyses informed projections of potential inundation under various sea-level rise (SLR) scenarios: 1, 2, 5, and 10 m. Key findings revealed a negative average coastline change rate of -0.27 m/year from 1985 to 2022, indicative of erosive trends likely accelerated by human activities. Long-term projections for 2032 and 2042 anticipate continued erosion in areas identified as highly vulnerable. The SLR scenario analysis underscores the urgent need for adaptive climate measures; while a 1- or 2-meter SLR presents limited immediate effects, a 5-meter rise could lead to significant inundation across key sectors, including urban and agricultural landscapes. The projected severity of a 10-meter SLR necessitates immediate, comprehensive interventions to safeguard both natural and human systems.

Environmental implications of three Pleurotus strain growths for water remediation in the perspective of climate change in New Egyptian Delta

Recently, the integrated different interdisciplinary studies derived the environmental solutions of the climate change impacts (e.g., cultivation, wastewater treatment, and managing groundwater resources) (Mesalhy et al. 2020, and Gobashy et al. 2021). Thus, this paper focused on the application of bioremediation to maximize the use of wastewater for new reclamation areas in the Northwest Egyptian desert (New Egyptian Delta (NED). In the NED project, the drainage water samples collected from Nile Delta drains will provide the main unconventional water resources for irrigation through the new Hammam canal. Therefore, three Pleurotus strains were grown moderately on two natural media, the first containing Salvia L. (sage) extract (MDA) and the second containing Thymus vulgaris L. (origanum thymus Kuntze, Thymus collinus Salisb) (TDA) extract replacing potato infusions in standard PDA. Pleurotus ostreatus (Jacquin; Kummer) strain records the highest growth among the three tested fungi on modified media. PO records 4.49 and 4.41 cm on (MDA) and (TDA), respectively. There is a marked decrease in the majority of heavy metal concentrations on sterile drainage water amended with PD broth and inoculated with three tested Pleurotus strains individually. At the end of the incubation period, Pleurotus ostereatus which expressed in abbreviation (PO) are more efficient in the removal of Al, Co, Cr, and Ni by 53.15, 95.87, 58.47, and 85.07%; respectively. Pleurorotus pulmonarius (Fr.) which symbolized (PP) is more potent in the removal of Cd, Si, Sn, Sr, and V by 70.37, 56.59, 41.19, 52.78, and 96.24%; respectively. Pleurotus floridanus (NZOR) which indicated as (PF) is actively over the former species in the removal of Ba, Fe, and Mo by 87.84, 46.67, and 97.34%; respectively. Cu, Mn, Pb, As, and Se could not be detected as the control sample recorded measurements below 0.009 mg L-1. An unexpected increase in Zn among the different treatments was detected from 05.04 to 07.01%.

Unraveling the hydrogeochemical evolution and pollution sources of shallow aquifer using multivariate statistical analysis and hydrogeochemical techniques: a case study of the Quaternary aquifer in Beni Suef area, Egypt

This study integrates multivariate statistical analysis and hydrogeochemical modeling to investigate the processes controlling the groundwater composition of a shallow aquifer where increased pumping rates and anthropogenic impacts were prevalent. Eighteen groundwater samples were collected and analyzed for major elements and selected heavy metals. The data were classified on the basis of multivariate statistical analysis into three clusters: C1 (Na-Cl facies), C2 (Ca-SO₄ facies), and C3 (Ca-HCO₃ facies). The application of factor analysis gave four factors affecting the groundwater chemistry, namely the salinization factor, anthropogenic/secondary enrichment factor, the secondary and the micro-nutrient fertilizers, and the aluminum fertilizer factor. The hydrogeochemical study of the groundwater revealed that the processes controlling the groundwater chemistry in the study area are mainly affected by the groundwater occurrence either to the east or to the west of Bahr Youssef Canal. Generally, the dominant hydrogeochemical processes affecting the groundwater are silicate weathering, ion exchange, irrigation return flow, gypsum applications in soil, and evaporation. The groundwater quality evaluation shows that water quality varies from fair to excellent for drinking purposes, where the best water is located in the northern and central parts of the study area. The suitability of groundwater for irrigation was evaluated using several indices indicating that groundwater is suitable for irrigation in the northwest and western parts of the study area. As some groundwater samples lie in high salinity classes on the US Salinity diagram, it is recommended to use this water for plants with good salt tolerance under good drainage conditions. The integration between the statistical and geochemical tools helps reveal the dominant processes through data reduction and classification.

Mapping of groundwater salinization and modelling using meta-heuristic algorithms for the coastal aquifer of eastern Saudi Arabia

The growing increase in groundwater (GW) salinization in the coastal aquifers has reached an alarming socio-economic menace in Saudi Arabia and various places globally due to several natural and anthropogenic activities. Hence, evaluating the GW salinization is paramount to safeguarding the water resources planning and management. This study presents three different scenarios viz.: real field investigation, experimental laboratory analysis (using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS), etc.), and artificial intelligence (AI) based metaheuristic optimization (MO) algorithms in Saudi Arabia. The main purpose of this study is to validate the obtained experimental-based analysis using hybrid MO techniques comprising of adaptive neuro-fuzzy inference system (ANFIS) hybridized with genetic algorithm (GA), particle swarm optimization (PSO), and biogeography-based optimization (BBO) for identification of GW salinization in the coastal region of eastern Saudi Arabia. Additionally, ArcGIS 10.3 software generates the prediction map based on ANFIS-GA, ANFIS-PSO, and ANFIS-BBO. Feature selection was assessed using the PSO algorithm, and four indices evaluated the estimated models, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation (SD). The simulated results are based on three variable input combinations, which showed that the ANFIS-PSO (MAE = 0.00439) algorithm had the highest accuracy (99 %), followed by the ANFIS-GA (MAE = 0.00767) and ANFIS-BBO (MAE = 0.0132) algorithms. Besides, Ca²⁺, Na⁺, Mg²⁺, and Cl^- were the most influential parameters. The accuracy also demonstrated the potential reliability of MO algorithms based on spatial distribution mapping. The employed approach proved to be merit and reliable tool for water resources decision-makers in the coastal aquifer of Saudi Arabia. This approach is believed to improve water scarcity as one of the essential targets for Goal 6 of Sustainable Development Vision 2030 and the Kingdom in general.

An Integrated Approach for Deciphering Hydrogeochemical Processes during Seawater Intrusion in Coastal Aquifers

For managing the freshwater in the worldwide coastal aquifers, it is imperative to understand the hydrogeochemical processes and flow patterns in the mixing freshwater/saltwater zone. The Egyptian Nile Delta aquifer is a typical example. The management of seawater intrusion (SWI) requires detailed investigations of the intrusion wedge and the dynamic processes in the mixing zone. Thus, a multidisciplinary approach was applied based on holistic hydrogeochemical, statistical analysis, and DC resistivity measurements to investigate the lateral and vertical changes in groundwater characteristics undergoing salinization stressor. The results of cross plots and ionic deviations of major ions, hydrochemical facies evolution diagram (HFE-D), and seawater mixing index (SMI) were integrated with the resistivity results to show the status of the SWI where the intrusion phase predominates in ~2/3 of the study are (~70 km radius) and the compositional thresholds of Na, Mg, Cl, and SO4 are 600, 145, 1200, and 600 mg/L, respectively, indicating that the wells with higher concentrations than these thresholds are affected by SWI. Moreover, the results demonstrate the efficiency of combining hydrogeochemical facies from heatmap and resistivity investigations to provide a large-scale characterization of natural and anthropogenic activities controlling aquifer salinization to support decision-makers for the long-term management of coastal groundwater.

Enhanced Adaptive Neuro-Fuzzy Inference System Using Reptile Search Algorithm for Relating Swelling Potentiality Using Index Geotechnical Properties: A Case Study at El Sherouk City, Egypt

The swelling potentiality is a vital property of fine-grained soils strictly related to the index properties and chemical composition. The integration of machine learning techniques and geotechnical parameters provided a new integrative approach for predicting the free swelling index (FSI) and the swelling pressure (SP). In this paper, an adaptive neuro-fuzzy inference system (ANFIS) using named Reptile Search Algorithm (RSA) is presented to predict the swelling potentiality for fine-grained soils in the foundation bed at El Sherouk city, Egypt. The developed predictive model, named RSA-ANFIS, used as input measured 108 natural fine-grained soil samples of index geotechnical parameters and chemical composition as input data and the measured data of the free swelling index and the swelling pressure as output data. To justify the performance of the developed model, a comparative study was carried out, and the results show that the developed RSA-ANFIS has a high performance over the competitive methods in terms of coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE). This new integrative approach is considered at the highly developed stage to predict and improve the analysis of multi-parameter soil behavior and could be applied in other objective variable datasets.