جستجوی مقالات مرتبط با کلیدواژه "silakhor plain" در نشریات گروه "آب و خاک"

The occurrence of successive droughts, along with increasing water needs and lack of proper management of water resources has caused a water crisis that has various environmental and economic consequences. In addition to the drought, the change in the cropping pattern towards water crops has also made the water crisis the first critical phenomenon in recent years in the community, which has a direct impact on the agricultural sector as the largest consumer of water. Therefore, optimizing the cropping pattern is one of the most important factors in managing water resources and coping with water shortages. In this study, to determine the optimal cropping pattern of major crops in Silakhor plain in the next three years using two approaches using Linear Programming and Meta-Heuristic Algorithms.

In the first step, in order to determine the optimal cropping pattern with the aim of maximizing farmers' incomes in the next three years and the limited water and land available, the amount of rainfall recharge is used as a criterion to determine the water exploitation interval and determine the minimum and maximum exploitation each year. In order to forecast rainfall, SARIMA time series models and Genetic Programming were used considering the data of the last 10 years in both seasonal and monthly modes, and according to RMSE and D.C. criteria, a better model was selected. Then, for each crop year, 100 exploitation scenarios were determined according to the amount of groundwater recharge caused by rainfall and the amount of exploitation in previous years. In the second step, Linear Programming was used to determine the optimal cropping pattern with the aim of maximizing farmers' incomes and limitations of exploitable water in each scenario and arable land. The price of each product is projected according to the average long-term inflation of the country, i.e., 20%, and the profit from the cultivation of each product was calculated as a proportion of the price of the product in each year by examining the previous years. Finally, the performance of three types of Static, Dynamic, and Classified Dynamics Penalty Functions into two algorithms, Differential Evolution and PSO was investigated to achieve the results obtained from Linear Programming. Static penalty functions use a constant value during the optimization process, whereas in dynamic penalty functions, the fines are modified during the process and depend on the number of generations. In the classified dynamics penalty, groups of violations are also determined, and the penalty of each response is determined according to the amount of violation of the restrictions and the generation number.

The results show that with increasing groundwater exploitation, farmers' incomes also increase; However, in the exploitation of more than 223.5, 222.2, and 225.1 million cubic meters for the cropping years 2020-2021, 2021-2022, and 2022-2023, respectively, the limitation of the total arable land has prevented the increase of the area under cultivation, and by increasing exploitation, farmers' incomes remain stable. Also, in order to cultivate four crops of wheat, barley, rice, and corn with the current area under cultivation in Silakhor plain, 142 million cubic meters of water is harvested annually from underground sources. By optimizing the cropping pattern for the four crops studied, with the current water exploitation, the income of farmers in the region will increase by 18%. In general, the PSO algorithm answers this problem much faster. The average number of iterations of the PSO algorithm to solve each scenario in this problem is 38% of the number of iterations of the Differential Evolution algorithm. Overall, in solving this problem, the PSO algorithm has performed better in 84% of the scenarios. In penalty functions, the best performance in both algorithms belongs to the classified dynamics, dynamic, and static penalty functions, respectively. By changing the penalty function from static to classified dynamics penalty function, the number of iterations of the Differential Evolution algorithm to achieve the Linear Programming solution is reduced by an average of 11%; In contrast, the PSO algorithm did not react significantly to the change in the penalty function, and its repetitions decreased by an average of only 3%.

The results show that the cropping pattern of the region is not optimal, and with the increase of water exploitation, it will move towards the cultivation of water products. Also, by optimizing the cultivation pattern of the region, farmers' incomes can be increased. Examination of Differential Evolution and PSO algorithms with three types of penalty functions also show that using the classified dynamics penalty function in the PSO algorithm can have good results.

Groundwater resource management depended on data obtained from the aquifer. Groundwater monitoring network can provide groundwater levels, but sometimes this information so much and not useful. This study develops a new multi-objective simulation-optimization model involving two objectives minimization of the total sampling cost for monitoring and maximization the spatially monitoring accuracy. Optimal design of groundwater network was considered in the Silakhor plain, regions of Lorestan, Iran. As the first step, a database includes of groundwater elevation in potential wells with use empirical Bayesian kriging (EBK) method in ArcGIS was produced. Inverse distance weighting (IDW) method used as simulation model and objective functions written in MATLAB software. Optimal groundwater monitoring network determines with preset conventions and finds by the non-dominated sorting genetic algorithm (NSGA-II). At final, a network with twelve observation stations that shown root mean square error (RMSE) value 0.605 meter. The optimal monitoring network, in comparison with the existing observation network, has been able to reduce the number of monitoring stations by 60%, improve the spatial distribution of stations, and predict appropriate zoning for unpredictable points.

Estimation of runoff from atmospheric rainfall has a special importance in hydrology studies, watershed management and soil science water conservation. In order to efficiently manage the watershed, the need to investigate the efficiency of hydrological models is more effective. The software package RRL is designed to simulate the runoff relationships of the basin and consists of five sub-conceptual models. In this study, conceptual models of AWBM, SACRAMENTO, SIMHYD, SMAR and TANK were used for Silakhor plain watershed (Rahim Abad station). The daily data of precipitation, evaporation and discharge were used in the statistical period of 1998 to 2018. For all conceptual models, from 1998 to 2000, as a balance period, from 2001 to 2013 as calibration and from 2014 to 2018, it was selected as the verification period. In the SIMHYD model, the Nash-Sutcliffe coefficient in the Multi Pattern Search Start method in the calibration and validation stages were 0.70 and 0.68, respectively, which indicates the relative ability of this model to simulate runoff among other models. In the TANK model, calibration and validation using Genetic Algorithm were 0.47 and 0.44, respectively, which had the lowest efficiency among the models. The results showed that in the different optimization methods, the Multi Pattern Search Start method provides better results.

Drought is a temporary and recurring meteorological event, originating from a lack of precipitation over an extended period of time. The success of drought preparedness and mitigation depends on timely information about drought onset and forecasting. This information may be obtained through continuous drought monitoring, which is normally performed using drought indices. Drought is an unpleasant, naturally occurring event caused by climate change that directly affects societies through changing their access to water resources. Among the numerous indices for drought intensity rating, the EDI and SPI have widespread applications. The SPI was computed by fitting a probability density function to the frequency distribution of the monthly precipitation records of each station. A drought event is considered to occur at a time when the value of the SPI is continuously negative and ends when the SPI becomes positive. The computation of the SPI drought index for any location is based on the long-term precipitation record (at least 30 years) cumulated over a selected time scale. This long-term precipitation time series is then fitted to a gamma distribution, which is then transformed through an equal probability transformation into a normal distribution. Positive and negative SPI values respectively indicate wet conditions (greater than median precipitation), and dry (lower than median precipitation). In most cases, the probability distribution that best models observational precipitation data is the Gamma distribution. Unlike most other drought indices, the EDI in its original form is calculated with the daily. The resulting EDI value represents standardized value for currently utilizable water resources, considering the continued dry period. If a negative DEP continues for more than 1day, the addition period of EDI will increase as long as the continued days. This variable addition period is limitless. The nature of genetic programming allows the user to gain additional information on how the system performs, i.e., gives insight into the relationship between input and output data. The GP is similar to genetic algorithm (GA) but unlike the latter, its solution is a computer program or an equation as against a set of numbers in the genetic algorithm. So, GP is more attractive than traditional GA for problems that require the construction of explicit models. The GP thus transforms one population of individuals into another one, in an iterative manner by applying operators. In evolutionary computation, it can distinguish between three different types of operators which are named crossover, reproduction, and mutation. M5 model tree approach is based on the principle of information theory that makes it possible to split the multi-dimensional parameter space and generate the models automatically according to the overall quality criterion. It allows for variation in the number of models created. The splitting in the M5 modal tree approach follows the idea of decision tree, but instead of the class labels, it has linear regression functions at the leaves, which can predict continuous numerical attributes. Model trees generalize the concepts of regression trees, which have constant values at their leaves. Therefore, they are analogous to piece-wise linear functions (and hence nonlinear). Computational requirements for model trees grow rapidly with increase in the dimensionality of the data set. Model trees learn efficiently and can tackle tasks with very high dimensionality. The major advantage of model trees over regression trees is that model trees are much smaller than regression trees and regression functions do not normally involve many variables. This research used precipitation data on two basins in Hamedan and Lorestan Provinces to calculate the SPI and EDI indices for monitoring drought. The genetic programming model and M5 model trees were used to predict the occurrence of drought in these two basins. It was found these models had good capability in predicting drought and enjoyed high accuracy in solving prediction problems. Another advantage of these models is that they use simple equations for predicting the phenomena under study. In the best-case scenario, the coefficients of determination for the EDI index in the M5 model trees and in the genetic programming model were 0.97 and 0.95, respectively. Moreover, the coefficients of determination for the SPI index in the M5 model trees and in the genetic programming model, in the best-case scenario, were 0.93 and 0.83, respectively. This suggests the M5 model trees are more accurate compared to the genetic programming model and enjoy relative superiority because they are simpler and more understandable than the genetic programming model.

In most studies on the distribution of soil carbon, three-dimensional distributions of soil inorganic carbon were neglected. Calcium carbonate is the most common carbonate in arid and semi-arid soils. Information on its spatial three-dimensional distribution is very important to determine factors controlling its distribution, to predict soil behavior and to improve soil management practice. This study aimed to map three-dimensional distributions of soil calcium carbonate (SCC) in an area of 3600 ha located in Silakhor plain (Lorestan province). An equal-area spline depth function (ESDF) was fitted to the measured SCC data of 103 pedons and the amounts of SCC at the five standard depths of the global soil map project were estimated.Then, ordinary kriging was employed to map the lateral distribution of SCC at all specified depths. Geostatistical analysis showed that spherical model was the best model representing spatial structure of calcium carbonate in all depths. All experimental variograms had a nugget to sill ratio less than 25 % which indicated strong spatial dependence for SCC. Anisotropy analysis indicated that ranges of variograms for all specified depths in the northwest-southeast direction were more than perpendicular direction. It indicated that SCC had more spatial dependence along Silakhor plain due to small variations in land use, slope and parent materials along the plain. Spline functions showed good performance in predicting vertical distribution of SCC (R2=0.88, RMSE=0.99). Both lateral continuous maps and spline functions indicated an increasing trend in SCC with increasing depth. In the eastern and southern parts, due to poor drainage and low leaching, SCC was high and its increasing trend with depths was not significant. Generally, results indicated that the combination of spline functions and geostatistical method is a promising approach to map three-dimensional distribution of SCC and to deal with some of the problem arising from legacy soil maps.

Since ground water and dwindling water resources is important for the operation of research and modeling is important. Assessment and prediction of groundwater level to help predict groundwater resources. The use of artificial intelligence methods based on the theory of data mining is used to predict the water table fluctuation. The support vector machine in artificial intelligence methods and the methods of geostatistical Kriging method has considerable precision in order to predict the time and location of the water table is level. In this study, the combination of support vector machine and Kriging model as a new way to predict when and where water table fluctuation in the plain area Silakhor is used. In the first phase, modeling when using support vector machine model data 11 piezometric wells in the region were carried out using support vector machine and secondly to predict the location of monthly data output of the first stage as an input earth model Statistics were used.
Data of 11 observation wells in the Silakhor plain data collected in the course of the past ten years in both normal and abnormal SVM model were used as input. Using the software Matlab function algorithm support vector machine was configured as an input at each stage of a well is this model. In addition, the maximum error in the calculation of the wells with 0.2 Keyvareh is due to lack of observation data in interval or lack of access to the region is to read water level. Absolute estimation interpolation and location estimate is a major feature. This means that estimates the quantity of sampling points is equal to the measured value and variance estimate is zero. In fact this model to estimate the amount of variance minimizes unknowns. Thus, the curves are drawn based routing and thus go beyond the boundary drawn. This feature makes the model the spatial distribution of data which are dependent on terrain, Kriging in calculations of high accuracy. Locate underground water level role in reducing the cost of drilling a well in the region. This way you can reach the height of water to be achieved in the region or even decline or rise of water table revealed
The ability of Support Vector Machine, and of course, a relatively new as a useful tool in water resource management to predict fluctuations in groundwater levels were evaluated in Silakhor plain. According to the accuracy of this method in predicting groundwater level can be comprehensive and appropriate program management discussion groundwater resources to be expected. In this study, using geostatistical methods in the area of water resources in plain Silakhor predicted with high accuracy are discussed. The most important issue in the analysis of spatial-temporal data to determine the dependence structure of the data. Whatever the choice of models and model are more accurate, the prediction will be more accurate. In terms of predicting when it should be noted that the time for the distance away from the last viewing time predicted prediction accuracy will be reduced. According to the study area in the last ten years has been well observed that in the best case scenarios predicted by the model, the highest coefficient of determination for wells Chughadun (DC = 0/96), sugar (DC = 0 / 94) and Valian (DC = 0/93) was calculated, which represents a hybrid model to predict groundwater level is the wells could be decided.

Identification of factors affecting hydrological processes plays an important role in water resources management. In this study, the effects of hydrological parameter variations such as rainfall, temperature and runoff time series are prioritized on the groundwater level fluctuations for the Silakhor plain using cross and coherence wavelet transforms. Cross wavelet transform and coherence wavelet transform estimate the interaction and time lag between two time series relatively. These transformations indicate that two time series have depended on period and phase lag. For this purpose, the time series of groundwater level- runoff, groundwater levelrainfall and groundwater level-temperature have entered into the cross wavelet transform and coherence wavelet transform algorithm mutually to determine the effects and interaction between these time series. The results show that runoff time series as the human activity factors with the mean wavelet correlation coefficient estimated as 0.83 is more effective than rainfall and temperature time series whereas the climate change factors are with the mean wavelet correlation coefficients of 0.52 and 0.58, respectively concerning groundwater level decline of the Silakhor plain. Thus, the human activity factors are more effective than the climate factors in declining the groundwater level in this plain.

Quantitative, continuous and three-dimensional soil data at appropriate scales are prerequisites for modeling of natural resources and environment. Despite the importance of soil texture, its legacy soil maps are often provided for the surface layers in which vertical and lateral variations of soil properties are not considered. The combination of digital soil mapping (DSM) and soil depth functions is an alternative tool to cope with these problems, especially in countries with limited data such as Iran. Therefore, equal-area spline depth function and DSM techniques were employed to assess the vertical and lateral distribution of soil texture in Silakhor Plain, located in Lorestan province, western Iran. By fitting the depth function to the measured clay, silt and sand percent in 103 sites to a depth of one meter, their estimated percents were obtained at five standard soil depths of Global Soil Map project (0-5, 5-15, 15-30, 30-60 and 60-100 cm). Also artificial neural network model was employed to predict lateral distribution of soil texture fractions using the auxiliary variable derived from satellite image and digital elevation model (DEM) in the standard depths. The results of the sensitivity analysis showed although the relative importance of auxiliary variables in predicting soil texture was different according to the depth and texture fractions, the performance of artificial neural network in upper layers was more than lower layers. R2 values for clay, silt and sand and from the top to the bottom were 0.73 to 0.49, 0.43 to 0.76 and 0.26 to 0.68, respectively. Results also showed, for estimating soil texture, auxiliary variables derived from satellite image were more important in surface layers and of DEM were more important in subsurface layers.