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Articles by Karim Solaimani
Total Records ( 8 ) for Karim Solaimani
  Karim Solaimani , Saeid Modallaldoust and Sedigheh Lotfi
  Problem statement: Land use change has transformed a vast part of the natural landscapes of the developing world for the last 50 years. Land is a fundamental factor of production and though much of the course of human history, it has been tightly coupled with economic growth. Soil erosion by water is one of the most important land degradation processes in the Mediterranean basins. The unplanned land use change within and near a fast growing agricultural land in Neka River Basin, led to an accelerated erosion of soil in the area.
This study aims to find the relationships between land use pattern, erosion and the sediment yield in the study area. The land use coefficient (Xa) has applied in the model of Erosion Potential Method (EPM) to forecast the effect of the land type to reduce the erosion. Land cover and land use change was projected for the next decade using topography, geology, land use maps and remote sensing data of the study area.
Results: The results of this study indicated that the total sediment yield of the study area has notably decreased to 89.24% after an appropriate land use/cover alteration. The estimated special erosion for the Southern Neka Basin is about 144465.1 m3 km-2 where after management policy is predicted 15542.9 m3 km-2 year-1, therefore the total difference for the study area has estimated about 128922.2 m3 km-2 year-1.
The land use changes assessed among the different land cover classes. It is important to mention that conducting of the present study a very severe land cover changes taken place as the result of agricultural land development. These changes in land cover led to the forest degradation of the study area. Relationship between land-use changes and agricultural growth offered a more robust prediction of soil erosion in Neka watershed.
  Karim Solaimani
  The present study aims to utilize an Artificial Neural Network (ANN) to modeling the rainfall-runoff relationship in a catchment area located in Iran. The study illustrates the applications of the feed forward back propagation for the rainfall forecasting with various algorithms with performance of multi-layer perceptions. The type of used data in ANN environment was 17 years monthly hydrometric and climatic data. For the operated model 14 years but for the validation/testing of the model 3 years data was applied. The results of this study explored that the capabilities of ANNs and the performance of this tool would be compared to the conventional approaches used for stream flow forecast. The estimated statistical results of the Root Mean Square Error (RMSE) and coefficient of determination (r) measures were calculated foe the used models of 1, 2 and 3 consequently: 2.5, 0.47; 1.57, 0.96; 0.2, 0.998. The results extracted from the comparative study indicated that the Artificial Neural Network method is more appropriate and efficient to predict the river runoff than classical regression model. Efficiency of the used model 1 is facilitated for regular temperature data as input component with using two stations, model 2 for precipitation with using five stations and , model 3 for rainfall, average temperature and flow data as participation with using six stations. It is concluded that model 3 provided more accurate and satisfied results than the other used models.
  Karim Solaimani and Zahra Darvari
  This study aims to development of the Kasilian indicator river flow forecasting system using Artificial Neural Network (ANN). In this study the performance of multi-layer perceptrons or MLPs, the most frequently used artificial neural network algorithm in the water resources literature, in daily flow estimation and forecasting was investigated. Kasilian watershed in Northern Iran, representing a continuous rain-fall with a predictable stream flow events. Division of yearly data into four seasons and development of separate networks accordingly was found to be more useful than a single network applicable for the entire year. The used data in ANN was hydrometric and climatic daily data with 10 years duration from 1991 to 2000. For the mentioned model 8 years data were used for its development but for the validation/testing of the model 2 years data was applied. Based on the results, the L-M algorithm is more efficient than the CG algorithm, so it is used to train 6 ANNs models for rain fall-runoff prediction at time step t+1 from time step t input. The used network in this study was MLP with BP (back propagation) algorithm.
  Shokoufeh Salimi , M. Reza Ghanbarpour , Karim Solaimani and Mirkhalegh Z. Ahmadi
  In this research, a methodology was applied to integrate hydraulic simulation model, HEC-RAS and GIS analysis for delineation of flood extents and depths within a selected reach of Zaremroud River in Iran. Floodplain modeling is a recently new and applied method in river engineering discipline and is essential for prediction of flood hazards. It is necessary to simulate complicated hydraulic behavior of the river in a more simple way, for the purpose of managing and performing all river training practices. In this research, steady flow was simulated along 3 km end of Zaremroud River, upstream of the Tajan River in North of Iran. Floodplain zonation maps were derived using integrating of HEC-RAS and GIS analysis. Delineation of flood extents and depths within the floodplain were conducted in different return periods. Critical flooding area along the river was distinguished based on the grid layer of flood depths. The results indicated that hydraulic simulation by integrating with GIS analysis could be effective for various kinds of floodplain management and different scenarios for river training practices and flood mitigation planning.
  Sedigheh Lotfi and Karim Solaimani

Problem Statement: Researches of quality of life are concentrated mainly on the urban nature in the recent years and the urban quality of life gained many attentions in empirical studies. The concept of urban quality of life is a multi-dimensional and complex issue. So, needless to say that this concept can be used in planning when there is an appropriate and reliable framework for measuring.
The present study tried to create a framework on the base of Analytic Hierarchy Process (AHP) for objective measuring of urban quality of life and then it would be applied for a comparative study of two northern cities of Iran.
The results showed that using analytic hierarchy process model creates opportunity to involving the different groups’ views of urban users with respect to their duties and functions in the stage of criteria weighting.
Conclusion: This process not only provided an appropriate bed for objective measuring of urban quality of life but it facilitated the participation of urban authorities in the process of measuring and analyzing the urban quality. Also one of the advantages of the model was its high level of clarity and simplicity which could be perceived by all urban decision makers.

  Karim Solaimani and S. Mostafa Mortazavi

Land subsidence is a phenomenon that involves the lowering or settling of the earth's surface due to various factors. The land subsidence due to groundwater withdrawal over the world has been seen in many areas. A decrease in ground water level would cause an increase in effective stresses at clay layers which results consolidation of lower layers. Since about 1980, it has been proven that Kerman Province subsidence in Iran is due to extensive ground water withdrawal. Overdraft of groundwater, an increasing of about 6 times since 1969 to 1999, has caused a decline of about 28 m in groundwater level. The rate of subsidence recently is about 5-15 cm. for decline of about one meter in groundwater level. In Rafsanjan area, many problems such as increase in the salinity of groundwater, land subsidence and consequently earth fissures and cracks in buildings are caused by groundwater withdrawal.

  Mahmud Habibnejad Roshan , Karim Solaimani , Abdol Piri , Mirkhalegh Ahmadi and Sedigheh Lotfi
  In this study, first by using Smirnov-Kolmogorov method, the consistency of data was applied in order to optimize the relationship between water and sediment discharge rates in Amameh indicator watershed of Iran. After the consistency and authenticity of data were confirmed, by means of daily mean discharge and a software called Technical Hydrology (TH), monthly hydrograph was sketched for total period of 1969-2000 in Kamarkhani station in Amameh watershed outlet. Then, different models were tested using the equation of sediment transport and considering hydrological, climatic and biological parameters such as hydrograph situation, classification of discharge rate and the time of flow measurement. In all models, the regression relationships between the rates of water and sediment discharges were established. To choose an optimized model, the sum of the error sum of squares index was used. According to the index, the least sum of squares shows the optimized model. The results showed that the common model in which only one equation is used as a sediment rating equation has the highest error in estimation of suspended sediment. But, a model in which the data are separated based on wet and dry months and classification of the discharge rates, has the lowest error sum of squares and is considered as the optimized model.
  Karim Solaimani and Zahra Darvari
  Hydrology and climatic monthly data's influence on training of Artificial Neural Networks (ANNs) for monthly rain fall prediction is investigated. For improved computed performances, efficiencies of the Conjugate Gradient (CG) and Levenberg-Marquardt (L-M) training algorithms are compared. The rain fall-run off influence is studied for a watershed in Northern Iran, representing a continuous rain fall-run off with stream flow regime occurring. The used data in ANN was hydrometric and climatic monthly data with 31 years duration from 1969 to 2000. For the mentioned model 27 year's data were used for its development but for the validation/testing of the model 4 years data was applied. Based on the results, the L-M algorithm is more efficient than the CG algorithm, so it is used to train six ANNs models for rain fall-runoff prediction at time step t+1 from time step t input. The used network in this study was MLP with B.P. (back propagation) algorithm. Model 1 uses enabled rain fall data as input dimension with use tree station, Model 2 uses enabled rain fall and average temperature, Model 3 uses enabled rain fall, average temperature and stream flow at time step t-1 and Model 4 uses enabled rain fall and stream flow at time step (t, t-1, t-2), Model 5 uses enabled rain fall and stream flow at time step (t, t-1, t-2.t-3) Model 6 uses enabled rain fall, average temperature and stream flow at time step (t-1, t-2). Validation stage Root Mean Square Error (RMSE), Root Mean Absolute Error (RMAE) and Correlation Coefficient (R) measures are: 0.07, 6x10-4, 0.99 (Model 1); 0.1, 9x10-4, 0.99 (Model 2); 0.01, 9x10-5, 1(Model 3); 0.005, 6x10-5, 1(Model 4); 0.001, 0.7x105, 1 (Model 5); 0.001, 6x10-5, 1 (Model 6) and, respectively. The influence of rain fall and stream flow at time step (t, t-1, t-2) on improved Model 4 performance is discussed.
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