One of the most important recent challenges in solid waste management throughout the world is site selection of sanitary landfill. Commonly, because of simultaneous effects of social, environmental, and technical parameters on suitability of a landfill site, landfill site selection is a complex process and depends on several criteria and regulations. This study develops a multi-criteria decision analysis (MCDA) process, which combines geographic information system (GIS) analysis with a fuzzy analytical hierarchy process (FAHP), to determine suitable sites for landfill construction in Iranshahr County, Iran. The GIS was used to calculate and classify selected criteria and FAHP was used to assess the criteria weights based on their effectiveness on selection of potential landfill sites. Finally, a suitability map was prepared by overlay analyses and suitable areas were identified. Four suitability classes within the study area were separated, including high, medium, low, and very low suitability areas, which represented 18%, 15%, 55%, and 12% of the study area, respectively.

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.

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.

In this study, a qualitative and quantitative change of groundwater resources of Damghan Basin was analyzed. Fifty-four active piezometric wells was used in the region which mostly suffer from shortage of drinkable water standard. Physical properties of 59 quality wells have been evaluated in order to determine the quality of groundwater by measuring factors such as pH, hardness, chloride content, electrical conductivity and total dissolved solid (TDS). By physical/chemical analyses, it was concluded that results for selected resources of Damghan Basin have been more comprehensive during 2008-2009 in comparison with other periods because scattered data cover all areas of the basin. By comparing the results with drinking water quality standards (WHO), assessment of water resources were conducted as acceptable to unsuitable. Hydrochemical perspective drawn using Piper diagram indicates that most of the selected water resources in the area contain chloride constituents. However, Na% values indicate that just 83 % of samples are permissible for using in irrigation purposes.

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.

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