Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

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

Armourstone Quality Analysis for Coastal Construction in Chabahar, Southeast Iran

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

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

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

Machine Learning-Based Assessment of Watershed Morphometry in Makran

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

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

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

Armourstone Quality Analysis for Coastal Construction in Chabahar, Southeast Iran

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

Improvement of pavement engineering properties with calcium carbide residue (CCR) as filler in Stone Mastic Asphalt

Characteristics of pavement material are crucial factors in improving the stability and durability of asphalt pavement due to the poor performance of traditional asphalts. Reusing industrial waste in asphalt concrete production can address these concerns, reduce environmental problems and preserve resources. There have been few studies on the effects of using industrial waste materials on Stone Mastic Asphalt (SMA) properties. This paper examines the impact of Calcium Carbide Residue (CCR) as a filler in SMA. The study evaluates control samples, those with Rock Powder (RP) and Ordinary Portland Cement (OPC) filler, and those with CCR using Marshall stability (MS) and Indirect Tensile Strength (ITSdry and ITSsat) tests. Results show positive effects of CCR and RP on asphalt sample strength. The highest MS, MQ, and TSR values were observed in samples containing CCR80 %+RP20 %. These indices increased 45 %, 35 %, and 51 %, respectively, compared to control samples. The alkaline CCR material forms strong bonds with acidic bitumen, producing asphalt more resistant by 97 % compared to control samples. SMA modified with CCR + RP was also found to be less sensitive to water damage than traditional SMA with RP or OPC filler. The rough texture of CCR may positively affect the strength and durability of asphalt mixtures against moisture damage. Using CCR as a filler in SMA can enhance pavement engineering properties, reduce production costs and environmental problems, and develop sustainable asphalt mixtures for practical application. The main novelty of this research is the use of CCR and RP combination in SMA mixtures

Armourstone Quality Analysis for Coastal Construction in Chabahar, Southeast Iran

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

Identification of Suitable Site-specific Recharge Areas using Fuzzy Analytic Hierarchy Process (FAHP) Technique: A Case Study of Iranshahr Basin (Iran)

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

Identification of Suitable Site-specific Recharge Areas using Fuzzy Analytic Hierarchy Process (FAHP) Technique: A Case Study of Iranshahr Basin (Iran)

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