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While fine glass powder is a promising precursor for geopolymer production, its preparation is time-consuming and expensive. This study investigates using six different particle sizes of recycled glass – three uniform (fine and coarse) and three hybrid (combinations) – to reduce processing costs and optimize geotechnical and microstructural properties of clay-based geopolymers. Glass content varied from 0 % to 30 % by dry weight of soil, and a sodium hydroxide (NaOH) solution as an alkaline activator was used at three molar concentrations (2 M, 4 M, 6 M). A series of unconfined compressive strength (UCS), direct shear, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Fourier transform infrared spectroscopy (FTIR) tests were conducted on samples. Results demonstrate that hybrid glass gradations containing both large (0.075–0.3 mm) and small (<0.075 mm) particles significantly improve the geotechnical properties compared to those with uniform fine particles, attributed to the enhanced dissolution of fine particles in the presence of coarser particles. The observed trends highlight the importance of combining particle sizes for optimal performance. This approach offers a more economical and effective alternative to traditional fine glass powder in clay soil geopolymers.

The authors regret to make a corrigendum to our paper “Innovative mapping of groundwater redox status and cation exchange conditions in a GIS environment,” published in the May 2024 issue of the Groundwater for Sustainable Development. We have discovered an error in our paper, where the Declaration of interests and the CRediT authorship contribution statement are incomplete. The Declaration of competing interest for the article is as follows. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mojtaba Zaresefat reports that article publishing charges were provided by Utrecht University. Mojtaba Zaresefat reports a relationship with Utrecht University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The Credit authorship contribution statement for the article is mentioned below. Mojtaba Zaresefat: Investigation, Methodology, Visualisation, Formal analysis, Validation, Writing the original draft, Review & editing, Data curation, Conceptualisation. Walter Schenkeveld: Review & editing, Data validation, Supervision. Reza Derakhshani: Review & editing, Data curation. Jasper Griffioen: Resources, Project administration, Data validation, Supervision, Review & editing. The authors would like to apologise for any inconvenience caused.

This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R2), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.

The authors regret to make a corrigendum to our paper “Innovative mapping of groundwater redox status and cation exchange conditions in a GIS environment,” published in the May 2024 issue of the Groundwater for Sustainable Development. We have discovered an error in our paper, where the Declaration of interests and the CRediT authorship contribution statement are incomplete. The Declaration of competing interest for the article is as follows. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mojtaba Zaresefat reports that article publishing charges were provided by Utrecht University. Mojtaba Zaresefat reports a relationship with Utrecht University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The Credit authorship contribution statement for the article is mentioned below. Mojtaba Zaresefat: Investigation, Methodology, Visualisation, Formal analysis, Validation, Writing the original draft, Review & editing, Data curation, Conceptualisation. Walter Schenkeveld: Review & editing, Data validation, Supervision. Reza Derakhshani: Review & editing, Data curation. Jasper Griffioen: Resources, Project administration, Data validation, Supervision, Review & editing. The authors would like to apologise for any inconvenience caused.

Iranian water security is threatened by groundwater (GW) degradation. The excessive use of GW for agriculture in Iran is degrading these resources. Livestock waste disposal and sewage irrigation are also major contributors. Nitrate (NO3) contamination in GW is a growing global concern, posing serious health and environmental risks. Soil can easily leach NO3 into GW, causing long-term contamination. Understanding the temporal and spatial patterns of NO3 pollution is vital in protecting human health and establishing safe drinking water limits. Choosing an appropriate interpolation method is crucial for creating a reliable spatial variability map, which is essential for environmental research and decision-making. This study used 85 GW samples collected over four periods to create interpolated maps and examine the spatial variability of NO3 levels. Spatial interpolation methods were performed using the geostatistical tool within ArcGIS Software. The results showed that Empirical Bayesian Kriging (EBK) was the most effective of the five evaluated interpolation methods, although the performance of each method varied depending on the period sampled. Therefore, the choice of interpolation method should be tailored to the study’s specific needs and the characteristics of the data being interpolated. The EBK method produced interpolation maps that illustrated the spatial distribution of NO3 concentrations, both within and exceeding the recommended guidelines. Interpolation methods can assist in creating spatial maps of NO3 concentrations, identifying pollution sources, and developing targeted management strategies. These maps demonstrate the potential impact of human activities on the observed patterns. A thorough understanding of Iran’s current GW quality is very important and valuable for management and policymakers.

This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The study area was analyzed by extracting watersheds from a Digital Elevation Model (DEM) and calculating eight morphometric indices. The morphometric parameters were normalized using fuzzy membership functions to improve accuracy. The performance of the machine learning algorithms is evaluated by mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R2) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R2 value of 0.974, MSE of 4.14 × 10−6, and MAE of 0.0015. The results of the machine learning algorithms were compared to the tectonic characteristics of the area, indicating the potential for utilizing the ANN algorithm in similar investigations. This approach offers a novel way to assess watershed morphometry using ML techniques, which may have advantages over other approaches.

A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater.

Natural stones (armourstones) of varying sizes and qualities are frequently used to construct breakwaters to protect coastal engineering structures from wave actions for economic reasons. Time-related armourstone deterioration in the form of abrasion and disintegration may result in structural damage. Therefore, it is necessary to investigate the performance and quality of the armourstones, which should be robust and long-lasting. The study aimed to examine the quality of two distinct types of rocks from three breakwaters used as armourstones in the Chabahar region and compare the results to the observed field performance. This study aimed to illustrate why it is crucial to characterise rocks thoroughly before deciding which ones to use in a particular project and to evaluate how well current classification techniques account for the observed field performance of stones that may have complex geological compositions. The physical and mechanical properties of the rock were evaluated through both on-site observation and laboratory testing. The results indicated that the class of rocks used in the breakwater had a wide range of suitability ratings. It was discovered that sedimentary rocks have the best water absorption and porosity properties. In addition, age is a positive factor, as the rate of destruction decreases with age. Component and particle size can also play a role in lithology, which is a significant factor in the rock’s durability. Also, the findings demonstrated that the marine organisms in the rock component play an important role in the stability of these structures, even though rock mass breakwaters are less qualified for breakwater construction as per international coastal engineering standards. According to the findings, a breakwater made of lumachel rock boulders, or alternatively sandstone boulders, will last the longest.

Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge.

Iranshahr Basin is located in the Sistan and Baluchistan province, subject to severe drought and excessive groundwater utilization. Over-reliance on groundwater resources in this area has led to aquifer drawdowns and socio-economic problems. The present study aimed to identify appropriate sites for Artificial Recharge Groundwater (ARG) in a single platform by applying GIS fuzzy logic spatial modeling. Three stages were performed. In stage one, nine factors affecting ARG were collected based on the literature review. In stage two, geology, soil, and land-use layers were digitized from the existing maps. Some layers such as rainfall, unsaturated thickness, water quality, and transmissivity data were imported to ArcGIS environments, and their surface maps were made by Ordinary Kriging (OK) method. In stage three, the parameters were standardized with the fuzzy membership functions, and the GAMMA 0.5 fuzzy overlay model was applied for aggregation parameters. Results showed that 72.8%, 16.7%, 7.7%, 2.5% of the areas were classified as unsuitable, moderate, suitable, and perfectly suitable sites for planning a groundwater recharge site. Subsequently, the minimum area required regarding the possible errors based on the literature review determined six sites (A–E) as areas with higher priority. Then, the recommended unsuitable/suitable sites were validated and omitted by using some more detailed views. Finally, two sites (E and F) were omitted, and four sites (A, B, C, D) were recommended for future artificial recharge planning.