13 results on '"Zaresefat, Mojtaba"'
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2. Innovative mapping of groundwater redox status and cation exchange conditions in a GIS environment
- Author
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Zaresefat, Mojtaba, Schenkeveld, Walter, Derakhshani, Reza, and Griffioen, Jasper
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- 2024
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3. Laboratory investigation of the effect of using metakaolin and clay on the behaviour of recycled glass powder-based geopolymer mortars
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Safarzadeh, Zahra, Pourabbas Bilondi, Meysam, and Zaresefat, Mojtaba
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- 2024
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4. Improvement of pavement engineering properties with calcium carbide residue (CCR) as filler in Stone Mastic Asphalt
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Zangooeinia, Peyman, Moazami, Danial, Bilondi, Meysam Pourabbas, and Zaresefat, Mojtaba
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- 2023
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5. Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands.
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Zaresefat, Mojtaba, Derakhshani, Reza, and Griffioen, Jasper
- Subjects
GROUNDWATER quality ,STANDARD deviations ,GROUNDWATER analysis ,SPATIAL variation ,INTERPOLATION ,KRIGING - Abstract
No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km
2 ) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4 . The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4 , Fe, PO4 , and NH4 . In contrast, SK performed worst for the species except for SO4 . We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling. [ABSTRACT FROM AUTHOR]- Published
- 2024
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6. A Novel Sustainable Approach for Site Selection of Underground Hydrogen Storage in Poland Using Deep Learning.
- Author
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Derakhshani, Reza, Lankof, Leszek, GhasemiNejad, Amin, Zarasvandi, Alireza, Amani Zarin, Mohammad Mahdi, and Zaresefat, Mojtaba
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CONVOLUTIONAL neural networks ,MACHINE learning ,CLEAN energy ,HYDROGEN storage ,STANDARD deviations - Abstract
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 (R
2 ), 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. [ABSTRACT FROM AUTHOR]- Published
- 2024
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7. Addressing Nitrate Contamination in Groundwater: The Importance of Spatial and Temporal Understandings and Interpolation Methods.
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Zaresefat, Mojtaba, Hosseini, Saeedeh, and Ahrari Roudi, Mohyeddin
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ENVIRONMENTAL research ,INTERPOLATION ,SEWAGE irrigation ,ANIMAL waste ,SEWAGE disposal ,ENVIRONMENTAL risk - Abstract
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 (NO
3 ) 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. [ABSTRACT FROM AUTHOR]- Published
- 2023
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8. Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review.
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Zaresefat, Mojtaba and Derakhshani, Reza
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GROUNDWATER management ,WATER management ,MACHINE learning ,ARTIFICIAL intelligence ,SOFT computing - Abstract
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. [ABSTRACT FROM AUTHOR]
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- 2023
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9. Machine Learning-Based Assessment of Watershed Morphometry in Makran.
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Derakhshani, Reza, Zaresefat, Mojtaba, Nikpeyman, Vahid, GhasemiNejad, Amin, Shafieibafti, Shahram, Rashidi, Ahmad, Nemati, Majid, and Raoof, Amir
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MACHINE learning ,ARTIFICIAL neural networks ,MORPHOMETRICS ,WATERSHEDS ,ARTIFICIAL intelligence - Abstract
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 (R
2 ) 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. [ABSTRACT FROM AUTHOR]- Published
- 2023
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10. Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin.
- Author
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Zaresefat, Mojtaba, Derakhshani, Reza, Nikpeyman, Vahid, GhasemiNejad, Amin, and Raoof, Amir
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ARTIFICIAL groundwater recharge ,ARTIFICIAL intelligence ,MACHINE learning ,GROUNDWATER recharge ,WATER supply ,ARTIFICIAL neural networks - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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11. Armourstone Quality Analysis for Coastal Construction in Chabahar, Southeast Iran.
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Ahrari-Roudi, Mohyeddin and Zaresefat, Mojtaba
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ROCK properties ,COASTAL engineering ,BUILDING stones ,SEDIMENTARY rocks ,STRUCTURAL engineering ,STRUCTURAL stability ,ENGINEERING standards ,STONE - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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12. Identification of Suitable Site-specific Recharge Areas using Fuzzy Analytic Hierarchy Process (FAHP) Technique: A Case Study of Iranshahr Basin (Iran).
- Author
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Zaresefat, Mojtaba, Ahrari, Mohiuddin, Reza Shoaei, Gholam, Etemadifar, Mahin, Aghamolaie, Iman, and Derakhshani, Reza
- Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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13. The Study of Potential Groundwater Recharge Zones Using GIS-Based Fuzzy Analytical Hierarchical Process (FAHP) Technique: A Case Study of Northern Dezful, Iran.
- Author
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Zaresefat, Mojtaba and Kalantari, Nasrolah
- Abstract
The study area is located in northern Dezful City, adjacent to Dez dam, in southwest of Iran and has variable groundwater potentials. Correct understanding of groundwater potential is essential for water resources management and sustainable use. Groundwater potential mainly depends on the amount of recharge. Lithology, slope, topography, weathering, porosity and vegetation are among the factors which influence recharge. Due to the large number of influencing factors on recharge and capabilities of Geographic Information System (GIS) in managing large amounts of spatiotemporal data from various sources, this tool was employed for preliminary studies. In order to increase judgment accuracy and flexibility in finding the highest natural recharge potential of the area, GIS was integrated with Fuzzy AHP. In the first place, factors influencing groundwater recharge were identified and weighted based on existing standards. The resulting zonation map was then used to identify areas with the highest penetration rate. Results showed that central and western parts of the area have the highest penetration rates, while areas adjacent to the northern parts have the lowest effect on groundwater recharge. It was also concluded that lithological variations caused by sedimentation conditions and fault activities, control groundwater potential among different geological formations and within each formation. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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