A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

Optimal extraction sequence modeling for open pit mining operation considering the dynamic cutoff grade

The cutoff grade problem is an important research challenge and vital optimization task in the yearly operational planning of open pit mines due to its combinatorial nature. Because of it's influenced by the economic parameters, the capacities of stages in the mining operation, mining sequence, and grade distribution of the deposit. Essentially, it asserts that the dynamic cutoff grade at any given period is a function of the ore availability and the needs of the mill at that period. Consequently, cutoff grades strategy and extraction sequence should be considered, simultaneously. Due to its goal, various attempts have been made to develop a computerized procedure for the extraction sequence of open pit mine. None of the resulting approaches appear to enjoy wide acceptance because of it's the numerous associated variables. A new model is proposed to overcome this shortcoming. This model solves the problem in the three steps: 1) the actual economic loss associated with each type of processing for each block, 2) the probabilities distribution and average grade for each type processing is computed from independent realization, and 3) each block with its expected economic loss is developed as a binary integer programming model. Using this model, the optimum extraction sequences in each period are identified based on the optimum processing decisions. A case study is presented to illustrate the applicability of the model developed. Results showed that the extraction sequences obtained using the suggested model will be realistic and practical. This model allows for the solution of very large problem in reasonable time with very high solution quality in terms of optimal net present value.     

Hsp70/Hsp90 co‐chaperones are required for efficient Hsp104‐mediated elimination of the yeast [PSI+] prion but not for prion propagation

The continued propagation of the yeast [PSI⁺] prion requires the molecular chaperone Hsp104 yet in cells engineered to overexpress Hsp104; prion propagation is impaired leading to the rapid appearance of prion‐free [psi^?] cells. The underlying mechanism of prion loss in such cells is unknown but is assumed to be due to the complete dissolution of the prion aggregates by the ATP‐dependent disaggregase activity of this chaperone. To further explore the mechanism, we have sought to identify cellular factors required for prion loss in such cells. Sti1p and Cpr7p are co‐chaperones that modulate the activity of Hsp70/Ssa and Hsp90 chaperones and bind to the C‐terminus of Hsp104. Neither Sti1p nor Cpr7p is necessary for prion propagation but we show that deletion of the STI1 and CPR7 genes leads to a significant reduction in the generation of [psi^?] cells by Hsp104 overexpression. Deletion of the STI1 and CPR7 genes does not modify the elimination of [PSI⁺] by guanidine hydrochloride, which inhibits the ATPase activity of Hsp104 but does block elimination of [PSI⁺] by overexpression of either an ATPase‐defective mutant of Hsp104 (hsp104^K218T/K620T) or a ‘trap’ mutant Hsp104 (hsp104^E285Q/E687Q) that can bind its substrate but can not release it. These results provide support for the hypothesis that [PSI⁺] elimination by Hsp104 overexpression is not simply a consequence of complete dissolution of the prion aggregates but rather is through a mechanism distinct from the remodelling activity of Hsp104. Copyright © 2009 John Wiley & Sons, Ltd.     

Introducing a dimensionless number as tank selector in hybrid solar thermal energy storage systems

Using hybrid energy storage system is a method for increasing the storage capability of solar thermal energy. If multiple energy storage devices with complementary performance characteristics are used together, the resulting system will be a ‘Hybrid Energy Storage System’. In other words, a Hybrid Energy Storage System (HESS) has several media available for storage at any time. In this way, increase in storable energy is obtained without increasing collectors’ area. When there are more than one storage mediums, the system should be able to choose the best medium for storing energy according to the conditions. In the previous works, an optimizer program was used to find the proper medium along time. But running a computer optimizer program is almost time consuming. So, in this work, a recognition parameter is introduced which helps to select the proper tank in different conditions. Using this dimensionless parameter, the hybrid storage system won’t be always dependent to the optimizer. The results have been checked theoretically and experimentally.     

Prediction of seepage water pressure for slope stability at the Gol-E-Gohar open pit mine

Slope stability accidents are one of the leading causes of destruction at open pit mining operations. Such interception of the seepage water results in the water inflow from the surrounding aquifer towards the mine excavations. In order to design an effective drainage scheme for an open pit mine, prediction of water inflow into the pit is essential. These changes have resulted in some failures and instability problems in different parts of Gol-E-Gohar iron open pit mine. It seems that main parameters which effect the failure and instability of the mine slopes are high pressure of groundwater and system of discontinuities (faults, joins, and bedding planes), which intersect the pit walls. The analysis results indicate that stability of the final pit slopes is sensitive to multi-planar failures and confined water in the walls pit is also a factor adversely affecting the stability. Problems associated with groundwater at the site were also assessed with the analyses of piezometric level and groundwater inflow.     

The orthogonal meshless finite volume method for solving Euler–Bernoulli beam and thin plate problems

In this paper a new method untitled “orthogonal meshless finite volume method” (OMFVM) is developed for solving elastostatic problems in Euler–Bernoulli beam and thin plate. In this method, the weak formulation of a conservation law is discretized by restricting it to a discrete set of test functions. In contrast to the usual finite volume approach, the test functions are not taken as characteristic functions of the control volumes in a spatial grid, but are chosen from a Heaviside step function. The present approach eliminates the expensive process of directly differentiating the OMLS interpolations in the entire domain. This method was evaluated by applying the formulation to a variety of patch test and thin beam problems. The formulation successfully reproduced exact solutions. Numerical examples demonstrate the advantages of the present methods: (i) lower-order polynomial basis can be used in the OMLS interpolations; (ii) smaller support sizes can be used in the OMFVM approach; and (iii) higher accuracies and computational efficiencies are obtained.     

Orthogonal meshless finite volume method applied to elastodynamic crack problems

An orthogonal meshless finite volume method has been presented to solve some elastodynamic crack problems. An orthogonal weighted basis function is used to construct shape function so there is no problem of singularity in this new form. In this work, for three-dimensional dynamic fracture problems, a new displacement function is used at the tip of the crack to give a new OMFVM. When the new OMFVM is used, the singularity of the stresses at the tip of the crack can be shown to be better than that in the primal OMFVM. High computational efficiency and precision are other benefits of the method. Solving some sample crack problems of thin-walled structures show a good performance of this method.     

On the use of the RMR system for estimation of rock mass deformation modulus

Estimation of rock mass deformation modulus is the subject of many studies in rock engineering research work. Although numerous predictive models have been developed for the estimation of the deformation modulus, they cannot be generalized for other sites because of inadequate accuracy. Furthermore, it is very valuable that the predictive models involve some accessible input parameters. The rock mass rating (RMR) is a well-known geomechanical parameter, which is usually determined to describe the quality of rock mass in rock engineering projects. In this study, five parameter ratings of the RMR classification system are used to predict the deformation modulus of rock mass in the abutment of the Gotvand earth dam. Statistical analysis and an artificial neural network are employed to present two new predictive models. Finally, probabilistic analysis is used to predict the rock mass deformation modulus, which overcomes the low accuracy caused by the inherent uncertainty in prediction. The results indicated that the parameter ratings used in the RMR classification system can predict the rock mass deformation modulus with a satisfactory correlation. However, the parameters don’t have the same influence on the rock mass deformability with the joint condition and the groundwater as the major and minor influencing parameters, respectively.