Spatial distribution of groundwater quality data and a reasonable monitoring network, which are usually collected from monitoring wells, are required for the management of groundwater resources. However, since the maintenance cost of groundwater monitoring networks is extremely high, an optimal design of those is necessary. This study aimed to find a qualitatively optimal monitoring network with a minimum number of wells in the Rafsanjan aquifer so that it could provide sufficient spatial distribution in terms of groundwater quality. For this purpose, electrical conductivity (EC) was selected as a quality parameter in the design of the monitoring network in this study. In the first step, to identify the risk and assess the vulnerability of the aquifer, the DRASTIC method was used. Then, the average Kriging standard deviation was used as a criterion for the determination of network density, and the GIS-based approach was analyzed. In this step, semi-variograms were tested to ascertain the best-fitted model accuracy measures, average standard error, root mean square error, and root mean square standardized error. The results showed that the spherical model is more reliable than other models due to the root mean square standardized error (RMSSE) being close to one, the average standard error (ASE) being close to the root mean square error (RMSE), and the less RMSE than other models. Also, based on cross-validation of data and a quality monitoring map resulting from the overlap of prediction and standard error maps with the DRASTIC map, 60 wells were sufficient as groundwater quality monitoring stations for the Rafsanjan aquifer. Removing 10 wells in different parts of the aquifer and adding 6 wells in the northwest of the aquifer will help to complete the quality monitoring network.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

In some instances, the occurrence of an event can be influenced by its spatial location, giving rise to spatial survival data. The accurate and precise estimation of parameters in a spatial survival model poses a challenge due to the complexity of the likelihood function, highlighting the significance of employing a Bayesian approach in survival analysis. In a Bayesian spatial survival model, the spatial correlation between event times is elucidated using a geostatistical model. This article presents a simulation study to estimate the parameters of classical and spatial survival models, evaluating the performance of each model in fitting simulated survival data. Ultimately, it is demonstrated that the spatial survival model exhibits superior efficacy in analyzing blood cancer data compared to conventional models.

Computer graphics offer various gadgets to enhance the reconstruction of high-order statistics that are not correctly addressed by the two-point statistics approaches. Almost all the newly developed multiple-point geostatistics (MPS) algorithms, to some extent, adapt these techniques to increase the simulation accuracy and efficiency. In this work, a scrutiny comparison between our recently developed MPS algorithm, the crosscorrelation-wavelet simulation (CCWSIM), and a well-known MPS algorithm, FILTERSIM, is performed. The main motivation to benchmark these two algorithms is that both exploit some digital image processing filters for feature extraction. Indeed, both algorithms compute the similarity (or dissimilarity) between data events in simulation grid and training image in the feature space. In order to compare the accuracy of the algorithms, some statistics such as facies proportion, variogram, and connectivity function are computed. The results obtained reveal an excellent agreement of the CCWSIM realizations with the training image rather than FILTERSIM. Furthermore, on average, the required simulation runtime for CCWSIM is at least 10 times less than that for FILTERSIM.

Nowadays, groundwater contamination is considered as one of the most important environmental issues. Because of its land use diversity, the Piranshahr Watershed (23385 ha) in East Azarbaijan Province was chosen as the study area for this research. Water quality samples were collected from 145 wells across the watershed. After variogram analyses and determination of spatial variations of the water quality parameters, different interpolation methods were applied to map the spatial distribution of the parameters. The methods with lowest RMSE were selected as the best interpolators. The results showed that, for pH, Ca, Mg and TH, radial-basis functions; for Na, inverse distance weighting (IDW); for TDS, exponential kriging; and for No3, ordinary kriging have the lowest RMSE values. The resulting maps indicated that, all parameters except Na and TH, have no limitation for drinking purpose. Spatial distribution of nitrate in groundwater indicated that highest concentrations of nitrate occur in the areas with high permeability and barren and agriculture land uses (east and south of the watershed). Results showed that water quality in the region has a degrading trend that is due to overexploitation of water for agriculture uses.

Soil saturated hydraulic conductivity is a key parameter needed in many projects including drainage. So it necessitates knowing about the spatial distribution pattern of hydraulic conductivity. However, to obtain the knowledge, it is needed to have a lot of field measurements carried out which is time consuming, tedius, and costly. Different types of kriging can be used for estimating and mapping hydraulic conductivity over a study area. However, the estimated results contain some uncertainties. Unlike kriging, stochastic simulation can be used to model the estimation uncertainty and incorporate it into the decision-making processes. In this paper, Sequential Gaussian Simulation (SGS) and non-parametric Sequential Indicator Simulation (SIS) approaches were employed to model the uncertainty attached to the hydraulic conductivity estimates in KheirAbad plain in Khozestan. A number of 200 equally probable simulated maps of hydraulic conductivity were generated through either of the methods. The results revealed that unlike the kriged map, the simulated maps could reproduce the histogram and semivariogram of the raw data, reasonably well. Regarding local uncertainty, the results showed that the kriging variance does not depend on the actual data values and so there is a limitation in its use. The accuracy plot and width of probability interval plot indicated that the uncertainty model obtained through SGS is more accurate than that obtained through SIS; however the goodness coefficient was slightly smaller for SGS (0.88) than for SIS (0.94).

In this study, electrofacies analysis has been done using MRGC (Multi-resolution graph-based clustering) method to be used in static modeling. Then, the resulting facies was modeled and compared together by applying different geostatistical stochastic algorithms in Petrel software. Two electrofacies classes including reservoir and non-reservoir facies were determined which were used for facies modeling. Seismic data was also applied for seismic facies construction and also to construct trend maps for appropriate facies distribution. In order to investigate the effect of five applied different geostatistical algorithms used in facies modeling on porosity distribution, the constructed facies models were used for porosity modeling. According to this study, the uncertainty of electrofacies modeling without applying seismic data increases which in turn reduces the accuracy of porosity models. In addition, electrofacies modeling considering the sequential indicator simulation (SIS) algorithm and applying the seismic trend maps, enhance the accuracy of the porosity model. Moreover, construction the of seismic facies is considered the best method for facies modeling to be used for porosity modeling due to the high correlation coefficient between acoustic impedance and porosity.