مقالات رزومه دکتر محمد اکبری

Land subsidence is a natural hazard that occurs gradually in many regions of Iran. Investigating the causes of subsidence is important in order to prevent its progression, and decisions should be based on the characteristics of each region. While the drop in groundwater level is an important factor affecting subsidence, there is no direct linear relationship between changes in groundwater level and subsidence due to the complexity of other influencing factors. Therefore, this research aims to investigate the relationship between subsidence and changes in groundwater levels in the Esfarayen region of plain during the period of 2015 to 2021. The study analyzed the time series of radar interferometry and the water level of piezometric wells in the area of Esfarayen. The results show that there is a correlation between the decrease in groundwater level and the increase in subsidence in the studied area. However, due to the complexity of the relationship between subsidence and its contributing factors, further investigation and more comprehensive models are necessary.

Predicting the potential of groundwater is very important for the systematic development and planning of water resources. The main purpose of this study was to develop ensemble machine learning models including random forest (RF), logistic regression (LR) and Naïve Bayes (NB) by random subspace Classifier (RS) algorithm to predict groundwater potential areas in Birjand plain. Therefore, for implementation, geo-hydrological data of 37 groundwater wells (Number of wells, location of wells and groundwater level or Water table) and 17 hydrology, topographic, geological and environmental criteria were used. The least squares support vector machine (LSSVM) feature selection method used to determine the effective criteria to increase the performance of machine learning algorithms. Finally, groundwater potential prediction maps were prepared using RF-RS, LR-RS and NB-RS models. The performance of these models evaluated using the area under the curve (AUC) and other statistical indicators. The results showed that the RF-RS hybrid model (AUC = 0.867) has a very high predictability for groundwater potential in the study area. It was also found that the elevation criterion is most important in predicting the groundwater potential in the study area. The results of the present study can be useful for making appropriate decisions and planning regarding the optimal use of groundwater resources.

Given the increasing importance of time, cost, and resource management, organizations are pushing for a knowledge base consisting of all the knowledge acquired during a project, and with the prior knowledge being put in place to maximize productivity. The purpose of this research is to investigate the effect of knowledge management on the delays of road construction projects in project-oriented organizations. The present study, after explaining the concept of knowledge management and identifying the causes of delays in road construction projects, examines the components of knowledge management and the impact of each of them on reducing the time latency of road construction projects through a questionnaire study method. Then, one of the sub-criteria of delay factors and its effectiveness from knowledge management was investigated. For this purpose, a statistical population consisting of active employers, consultants and contractors in the road construction sector of Southern Khorasan province was considered and parametric tests were used for inferential analysis. Findings show that there is a significant positive two-way relationship between knowledge management and reducing project time latency. This means that the more knowledge management is used in road construction projects, the greater reduction in project latency we will see. Among the components of knowledge management, knowledge creation has the greatest effect (17.42%) on reducing delays and based on the calculated results, it can be said that about 96.8% of delays in projects can be controlled by knowledge management. Also, according to the descriptive statistics obtained in the general case, it can be said that the most important factor affecting the delays are the contractors at the rate of 21.35% and then the employers at the rate of 20.76%.

Research on flood predicting models is one of the first steps in reducing flood damage and managing future floods in catchments. The aim of this study was to evaluate flood susceptibility in Birjand plain catchment through four machine learning models including support vector machine (SVM), J48 decision tree, random forest (RF) and Adaptive neuro fuzzy inference system (ANFIS). Therefore, in order to implement and validate the mentioned models, a list of flood-prone areas in the study area was prepared (42 flood-prone locations). In addition, 19 hydrogeological, topographical, geological and environmental criteria affecting flood occurrence in the study area were extracted to be used to predict flood susceptibility map. The results showed that the highest accuracy was related to the RF model (0.845) and the lowest accuracy was related to the SVM model (0.791). In addition, the validation of the results using the ROC curve showed that the most accurate values of flood susceptibility belong to the RF model (AUC = 0.958). The results of this study can be used to manage vulnerable areas and reduce flood damage.

Groundwater is one of the most valuable resources for communities, agriculture, and industry. In the present study, three new artificial intelligence models, including Modified Real AdaBoost (MRAB), Bagging model (BA), and Rotation Forest model (RF), have been developed by the Functional Tree Base Classifier (FT) model to predict groundwater potential in Birjand plain area. Therefore, for implementation, geo-hydrological data of 37 groundwater wells and ten factors of topography, hydrology, and geology were used. The performance of these models was evaluated using the area under the curve (AUC) and other statistical indicators. The results showed that although all the hybrid models developed in this study increased the prediction accuracy, MRAB-FT model (AUC = 0.742) has higher accuracy in predicting potential groundwater areas in Birjand plain. Accurate mapping of groundwater potential areas while maintaining a balance between consumption and operation will help feed the aquifer for optimal use of groundwater resources.

Drought is a natural hazard that has a significant impact on the economic, agricultural, environmental and social aspects. Therefore, monitoring and studying this phenomenon is important and vital. Much research has been done in this regard and based on indicators extracted from satellite data. In the present study, drought monitoring was performed based on the VHI index extracted from satellite data. This index is a combination of two indicators of vegetation status (VCI) and temperature index (TCI), which is used to calculate these two indicators of MOD13 and MOD11 products of MODIS sensor, which are related to normalized differential vegetation index (NDVI) and surface temperature, respectively. Land (LST), used in the period from 2010 to 2018. Evaluation of telemetry index (VHI) was done using standardized precipitation index (SPI), so that the correlation coefficient of annual values of SPI index, 14 synoptic stations of the study area with VHI index values in the same stations in the period 2010-2017 شد .The results showed a correlation coefficient between 0.64 and 0.81 in the years under review.

Groundwater is the main source of drinking water in the semi-arid regions of Iran. Therefore, monitoring the quality of this valuable resource is vital. Hence, the purpose of this study was to investigate and prepare a groundwater quality map of Birjand located in the semi-arid region of eastern Iran using GIS-based geostatistical analysis with Drinking Groundwater Quality Index (DGWQI). For this purpose, the collected groundwater quality data of 27 agricultural wells during the years 2014-2019 were used. The results of spatial analysis show that 63% of the aquifer area was in the appropriate quality category, 18% in the poor category, 10% in the very poor category and 9% of the aquifer area was in the non-drinking category. Sensitivity analysis indicated that Mg2+, EC, and TDS parameters with the highest mean change index of 18.98, 20.68 and 19.04 were the most sensitive parameters in calculating DGWQI, respectively. Error evaluation calculated by R2 and RMSE methods on conventional kriging and spherical variogram models showed good performance for spatial analysis of all parameters including DGWQI, Mg2+, EC, and TDS. The DGWQI map shows that the western and southwestern parts of the aquifer do not have good quality conditions for extracting drinking water.

Due to the growth of surface water pollution, this study intends to evaluate the behavior of self-purification and pollution removal in rivers by using numerical modeling. In this research, using data and information provided by water sampling in the Kardeh River, in Mashhad as a source for supplying drinking water in Mashhad city, we have investigated the river quality monitoring processes using the MIKE11 numerical software. In this model, boundary conditions include discharge, pollution (Ecoli) and water level, which has been done through field measurements. Measurements were performed in two time periods, one in April 2014 (indicating the high water season of the year) and the other in August 2014 (indicating the low water season of the year) by taking 12 samples of Kardeh river. The resulted outcomes of this research show that Manning roughness coefficient with the help of hydraulic model calibration in the study period is 0.058 and the river decay coefficient for Ecoli parameter in the warm season with a diffusion factor of 20 and diffusion coefficient of 0.5 is 0.08 and in the cold season with a diffusion factor of 5 and diffusion coefficient of 2 equals to 0.207. According to the results, the effect of Manning coefficient in estimating pollution is 90% and the share of other parameters is 10% in total. In order to calibrate the model, sensitivity analysis was used, which concludes from the above sensitivity analysis that the Manning coefficient has a great effect on modeling the spread and transmission of contamination.

In this study, the effect of polyethylene wax on improving the performance of asphaltic mixtures was investigated through laboratory testing. Experimental samples were taken from asphalt compacting in place containing polymer additives, especially polyethylene waxes. To investigate the role of polyethylene wax in reducing pavement fatigue cracks, samples with dimensions of 20 cm* 5 cm * 5 cm in two conditions without and with the amount of polyethylene wax and constant amount Asphalt ratio of 4.5 in all samples at different temperatures and load frequencies for fatigue were tested. Fatigue test results showed that for all three mixtures arranged with polyethylene wax in all samples, the pavement thickness after the test, for 0.09% wax, it was higher than that of the other asphalt mixtures. That is, the drop in the thickness of the pavement under the fatigue test is minimal, indicating an optimal weight percentage of the polyethylene wax executed in the pavement layer. So that the increase in the percentage of asphalt mixing with polyethylene wax from 0.05 to 0.09 percent, the decrease in the thickness of the pavement under the fatigue test decreases, but from 0.09 to 0.15 percent, the thickness drop increases. Thus, for artillery mixes with 0.09% polyethylene wax, the drop in the thickness of the asphalt pavement layer is less under fatigue testing, that is, the rate of cracking of asphalt armed with 0.09% of the polyethylene wax is minimal.

Water distribution networks are amongst the most important infrastructures of water supply projects, which attracts notable financial credits from the governments. As optimal design can significantly reduce the costs of such large projects, designers always try to minimize the costs while applying all the constraints and technical specifications of the project. Genetic algorithm is one of the most popular approaches for optimizations in the design projects. This research aims at evaluating the meta-heuristic methods used in designing the urban water distribution networks and to assess the genetic algorithm as an optimization mean for such methods. Given the heavy costs associated with water distribution network projects, it is very important how a model perform the cost evaluation. For the current study, the modeling was first performed by WaterGEMS software and the total cost was calculated based on the price list. Then, according to the objective function and cost criterion and with respect to constraints of speed and pressure in the network, optimization was performed and the final cost was obtained. The results of the proposed method showed that by optimization, the investment cost would decrease by about 14% compared to the original plan. Comparison was also made to other meta-heuristic algorithms which confirmed better performance of the proposed algorithm with regards to costs.

 Nowadays, most land use changes occur in urban areas, due to the growing population in cities and villages and the desire to live in urban areas. Urban rapid development in recent decades has led to large changes in the cities around and has had many environmental impacts. In this research, we evaluated land use changes and urban development simulation using satellite imagery and with neural network model and Markov chain auto-cells in Rasht city. For this purpose, Landsat satellite imageries were used from 2000, 2008 and 2017. After preprocessing the image and selecting the best band combination, the images were classified using the neural network method. Then the classified images were entered into the land changes model and predicted modeling output maps using the CA-MARCOVE method for 2027. The results obtained between 2000 and 2017 indicate that the area changes in urban lands, rice fields and forests were 9041.88, 7841.33 and 55.78 hectares, respectively, which were positive in Rasht city and negative in rice fields and forest and the projection map for 2027 with the CA-MARCOVE method also indicated a significant increase in urban use of 14105.04 hectares in the coming years. The results of this study indicate that the current trend of land use changes will lead to adverse environmental impacts and, consequently, irreversible socio-economic damage. Therefore, it is essential for the region planning and management unit to adopt a comprehensive approach to conduct future environmental problems and to curb the horizontal development of settlements in the area.

 The importance of land use as a key component in natural resource management, environmental change and a dynamic and affecting biological condition requires accurate quantitative and qualitative information to be provided and varied in the short term. (Triantakonstantis & Stathakis, 2015: 194; Akbari and Rezaei, 1397: 94). In the meantime, remote sensing data provide valuable multi-temporal data on the processes and patterns of land cover change and land use, and help to develop an understanding of the impact of human activities on natural resources. (Esfahanzadeh, 2016: 34). Urban development is a global phenomenon and one of the most important phenomena that has a great impact on both nature and human environment due to its many ecological and socio-economic aspects. The city of Rasht, like other urban areas, has undergone numerous changes in agricultural and horticultural uses and residential uses over many years. In this study, satellite imagery is used to evaluate land use changes and simulate urban development in the period 2000 to 2017, so that the results of the research can be of great help in micro planning. And provided the experts with a great deal to prevent environmental degradation.

In this study, using satellite imagery of land use changes and simulation of growth and development of Rasht city using neural network model and Markov chain automated cells. Landsat 2000, 2008 and 2017 images were used for this purpose. After image preprocessing and selecting the best band composition, the images were classified by neural network method. Selected classes include 7 classes, forest, man-made areas, paddy fields, sand, sea, ponds and vacant lots. The digital layers used to classify and apply Markov auto cells include: GPS capture points for image classification and accuracy assessment, proximity to main roads, river avoidance, distance from surrounding villages, slope And height. Then, the classified images were entered into the land change modeler and the model outputs were predicted by CA-MARCOVE for 2027.

 The results show that out of the total area of ​​man-made area increased, 3612 hectares were converted to paddy fields and 1 hectare to water use, 2138 hectares were made to man-made areas, 1646 hectares to the sea and 24 hectares to the Bayer area. In the present study, Markov chains and automated cell fusion methods were used to predict land use changes in Rasht. To do this using IDRISI software, three series of land use maps were prepared for the years 2000 to 2017. Finally, based on the factors involved in urban land use changes in the study area, the inputs of the automated cell model were selected as Table (1). The prediction is a function of the model inputs. Table 1: Input variables in the automated cell simulation model Row Variables affecting land use 1 near the main ways 2 distance from the river 3 elevation 4 slopes 5 distance from surrounding villages Source: Authors' Studies, 2018        Then, by calculating the Kramer coefficient in the model, one can obtain an estimate of the correlation of each variable with the existing land uses and hence its ability to predict land use changes. By repeating 10,000 times of trial and error in the multilayer neural network, calibration and conversion potential maps were generated in the images from 2000 to 2008 and 2008 to 2017. Following the acceptable accuracy of the model for prediction, using the CA-Markov model, the 2027 User Prediction Map was prepared for the study area shown in Figure (1).   Figure 1: Land use forecasting map of Rasht city using CA-Markov for 2027, Source: Research Findings, 2018.     Table 2. Land use area of 2027 using CA-Markov User class 2027 forecast area (ha) Jungle 0/13 Sands 0/9639 Water 974/26 rice field 4797/82 Man-made areas 14105/04 Wasteland 1599/03 Sea 6/39          As can be seen from Table 2, the area of most land uses, except for man-made areas and the sea, declined as forest land use from 1031/95, sandy land from 15/42, water from 22/333, paddy fields from 88/12/85 and wasteland from / 66. 3629 hectares decreased in 2000 to 0.13, 0.96, 97.26, 47.72 and 15.03 ha in 2027, respectively. In contrast, the land use area of the man-made areas increased sharply to 14105.04 hectares, while sea use increased by 6.39 hectares.

The use of Landsat satellite imagery is useful in terms of availability, duplicate coverage and lower cost of source data, as well as determining the extent of land cover changes and land use prediction using the models used in Research can be a good alternative to costly methods of discovering change in the shortest time possible. Other objectives of this study were to use satellite imagery and LCM tools to detect changes occurring in the region during the study years 2000–2008 and 2008–2017. Therefore, multi-layer neural network method was used to detect the changes. Examination of changes from 2000 to 2008 showed an increase in urban class area, with the city area increasing from 6793.91 hectares in 2000 to 8940.41 hectares in 2008. The highest increase in urban area was observed from 2008 to 2017 after image classification. During the study periods, paddy, forest and wilderness land use has been steadily declining, and vegetation use has had a protective role as urban land use. In this study, the prediction of physical growth in the city of Rasht in the coming years (2027) was investigated. This is how the 2017 forecast map was first derived using the CA-MARKOV model. Comparison of the results of the prediction map with that of the image classification showed high accuracy. The 2027 forecast map also shows a significant increase in urban land use by 14105.04 hectares in the coming years. Considering the results, it is possible to study changes in vegetation cover and to prevent its unnecessary changes and transformations. Because vegetation plays an important role in reducing environmental issues in urban areas. In contrast, the disappearance of vegetation causes severe environmental crises in relation to the rapid growth of urbanization and the formation of the thermal island of the city. As a result, vegetation is considered as an indicator of environmental sustainability in urban communities. Therefore, proper vegetation management is considered as an integral part of any sustainable urban development. Since degradation of vegetation and rising surface temperature can have adverse effects on the environment, identifying environmental sensitivities (crises) caused by this factor is essential as it can play an important role in urban development management.

Given the importance of groundwater resources and the water shortage crisis in Iran, a significant attention should be paid to these resources. One of regarding challenges is the resource pollution; therefore the vulnerable resources should be revealed based on suitable assessments to eliminate pollutions by the necessary actions. In the most cases, the groundwater pollution can be detected after the drinking water wells were polluted. Detection of vulnerable zones of an aquifer can be used for the groundwater pollution preventive actions. Different vulnerability assessment methods have been presented which one of the proper and well-known methods of overlaying indicator named SINTACS has been used in this research. The study aims assessing the qualitative vulnerability of Birjand plain aquifer, using SINTACS model for a statistical period of 2016-2017. Based on the results of this assessment, the SINTACS vulnerability zoning map indicates that 12.81 percent of the zone space has a vulnerability of medium to high level, 80.47 percent of the space has a high vulnerability level and 6.71 percent of the space has a very high vulnerability level. For the SINTACS model verification, the relation between vulnerability and Nitrate density values of 21 groundwater samples have been examined. The gained correlation coefficient between vulnerability index of SINTACS and Nitrate density was 71 which shows suitable correlation of this model with Nitrate density as groundwater pollution index.