multivariate regression

Net present value (NPV) index is one of the most important economic parameters in evaluating the mining investment projects. Considering the inherent uncertainty in effective parameters on the NPV in most of the mining projects, its precise estimation is a difficult process and application of the suitable methods is required. In this paper, fuzzy system, neural network and multivariate regression models are used for NPV determination in the Zarshuran gold mine project. In spite of the metal value, the effects of costs and discount rate on the NPV are also considered as an index for evaluation the Zarshuran gold mine project of Takab using the above models. One of the advantages of this research compared to the previous similar studies is the utilization of two new intelligent algorithms i.e., fuzzy system and neural network for modeling the NPV index with higher accuracy. These algorithms are efficient tools to solve the problems having ambiguity and uncertainty such as the mining investment projects and reduce the investment risks. Considering the management challenges in deciding on the economic justification of various projects and the existence of time and resource limitations, the use of these models helps clarify the economic future of a mineral investment project and make the final decision.

In this study, three new models including fuzzy system, neural network and statistical multivariate regression were used to determine the NPV and evaluate the effective parameters on this index in the Zarshuran gold mine investment project of Takab. The obtained results of the above models were compared with each other, with the real data, and with the similar previous studies. Gold price (main element), silver price (byproduct element) and discount rate were considered as input parameters for evaluating the NPV. Application of the fuzzy logic in NPV modeling, which is always accompanied by uncertainty, can lead to more realistic results. Also, the utilization of neural network can be effective due to its high capability in dealing with vague and noisy data that are somehow involved in the evaluation of mining projects.

Using the trial and error method, triangular and trapezoidal membership functions, Mamdani inference motor and center of gravity decentralization function were determined as the optimum parameters of the proposed fuzzy model. Also, a neural network with training function of Levenberg-Marquardt back-propagation type, transfer function of sigmoid logarithm kind, 3-5-10-1 structure and root mean square error of 0.0032 was found as an optimum network. Moreover, a statistical multivariate linear relation was proposed to predict the NPV. According to the performance evaluation indices including determination coefficient, absolute error and relative error, results of the proposed models were compared with each other, with the actual data, and with the previous similar investigations. The above comparison proved that the performance of both fuzzy and neural models in predicting the NPV is acceptable and they have a much better agreement with real data in comparison with the statistical model. However, the performance of fuzzy model is somewhat better than the proposed neural network models in this research and previous studies and its results are more consistent with the actual data. Finally, the sensitivity analysis of the fuzzy model results (due to its greater accuracy) are conducted using the cosine amplitude method (CAM). Accordingly, it was confirmed that the gold price has the highest effect and the discount rate has the least effect on the NPV index.

The present study proved the successful application of two new intelligent-based models including fuzzy logic and neural network algorithms to predict the NPV in the Zarshuran gold mine investment project. Also, a linear multivariate regression relation proposed to forecast the NPV index with a relative acceptable performance. Evaluation the results of proposed models showed that the fuzzy model performance was somewhat better than the proposed neural network models in this study and previous studies and both were much higher than the statistical model in predicting mining projects NPV. Based on the sensitivity analysis of the fuzzy model results (the most accurate proposed model), it was proved that the gold price and discount rate have the highest and lowest effects on the NPV, respectively. Considering the above results, it can be concluded that the proposed intelligent techniques in this research (especially the fuzzy model) can be used with good reliability in the evaluation of mining investment projects in order to cover their involved uncertainty and reduce the investment risk.

Dust storms are one of the natural phenomena that have affected many arid and semi-arid regions of the world in recent decades And it has increased significantly in the past years And as a result, it has had many harmful effects on the residents of the areas, so that the living conditions are very difficult in many areas due to the large amount of dust. Unfortunately, due to climate changes, including the decrease in rainfall, which on the other hand leads to the barrenness of the land surface and soil erosion, the conditions for the transport of fine dust can be provided, and when there are storms, a lot of fine dust is carried towards residential areas, and this affects It has a negative effect on the economy, health and environment. Fine dust enters the atmosphere affected by various factors including atmospheric conditions, characteristics of the earth's surface and characteristics (temperature, rain, wind, soil). Desert and devoid of vegetation are among the natural resources. Therefore, the most important factors affecting the intensity of dust are climatic changes and land surface conditions, so that with the decrease of rainfall and the decrease of water resources, especially in wetlands and the increase of barren lands, conditions prone to dust increase. Sistan plain is one of the important regions of the country which is very negatively affected by storms. Because, the lack of drainage of the Hamon wetland has led to the desertification of many areas of the wetland, and this has caused many of the wetlands to be transported to residential areas during dust storms, causing the destruction of agricultural and residential lands, damage to infrastructure, and many heart diseases. and be respiratory. Therefore, it is very important to investigate the factors affecting the intensity of dust in Sistan region so that necessary measures can be taken to manage and plan dust control. Therefore, the purpose of this research is to investigate the frequency of days with dust in relation to climatic variables (temperature, rainfall and wind speed) and water intake of Hamon lagoon.

In this research, the degree of correlation between the frequency of dusty days in relation to climatic variables and water intake of Hamon wetland in the Sistan plain in the period (2011-2021) was investigated. For this purpose, the average annual data of temperature, rainfall, wind speed and catchment area of Hamon lagoon were used in the studied time period. The data used were obtained from the meteorological station of Zabul. In order to use the data of the catchment area of Hamon lagoon, satellite images related to Landsat 7 and Landsat 8 satellites for 11 years (2011-2021) have been used.in order to prepare a map for extracting NDVI of water resources from Landsat satellite images related to the years 2011 to 2021 were used. The water resources index was used in the studied years. Then, the water layer was extracted by reclassification from the spectral index of each year and prepared as a Boolean layer of zero and one. Pearson's correlation coefficient was used in order to investigate the correlation between dusty days with climatic variables and the catchment area of Hamon lagoon. For modeling, multivariable regression was used, multivariable regression shows the change rate of one variable for other variables, and in other words, the rate of change in the dependent variable that occurs due to a unit change in the independent variable. In this method, a multi-equation A variable is used that summarizes the relationship between the dependent variable and the independent variables in a formula using the measured values. In this model, the number of days with dust is selected as the dependent variable, and the variables, temperature, rainfall, wind speed and water intake of Hamon lagoon are selected as independent variables. The coefficients of the equation for each variable are calculated and determined based on its importance in predicting the criterion variables. The degree of correlation between predictor variables is shown by coefficients.

The purpose of this research was to investigate the intensity of correlation and model the relationship between the frequency of days with dust storms and the water intake variables of Hamon lagoon, wind speed, rainfall and temperature in Zabul station. The results showed that the highest correlation coefficient with the number of days with dust is related to the wind speed with a value of 0.808, which shows the highest correlation. The correlation coefficient of temperature with the number of days with dust shows a significant and positive relationship of 0.422. The correlation coefficient of rainfall with the number of days with dust shows a significant and inverse relationship of -0.333, in addition to this, the frequency of dusty days with Hamon lagoon drainage has an inverse correlation coefficient with a value of -0.748. Because with the lack of dewatering of the Hamon wetland and the drying of the wetland bed and the reduction of vegetation, as a result of wind erosion, the wetland bed becomes the main centers of dust. And with the wind blowing, if the wind speed is high, a significant amount of sediments are transported from the wetland bed to the residential areas. Multivariate regression modeling between dust and the studied parameters showed that Hamon lagoon drainage and wind speed have a great effect on dust. According to the correlation coefficient of the studied variables, it was found that the water intake status of Hamon wetland has a greater effect on dusty days in the region than rainfall and temperature, so that with the increase of 1000 hectares, the water intake of Hamon wetland will decrease by about 0.3 units of dusty days. became. The results of multivariate regression modeling for dusty days and studied parameters showed that Hamon lagoon water intake and wind speed have a great effect on the frequency of dusty days. According to the value of R2, 61% of the dependent variable (number of dusty days) is explained by the independent variables (wetland drainage status, wind speed, temperature and rainfall) entered into the model.

Dynamic and static properties of the rocks are very important for designing geotechnical structures and modeling rock foundations. The main purpose of this paper is to present the regional and global relationships between the static and dynamic elasticity modulus with an experimental approach and to estimate the shear wave velocity of limestone by statistical methods and artificial neural network (ANN). For this purpose, petrographic, physical and mechanical experiments were first conducted on 80 limestone cores from the Karun 4 dam site. A database was then created using the literature data and compared with the results of this study. The results of statistical analysis show that the ratio of dynamic to static modulus of elasticity for the studied samples is 2.5. Also, the ratio of dynamic to static Poisson for these rocks was 1.41. The average value of the dynamic modulus obtained from the literature was equal to 19.90 GPa, which is less than the average value of the dynamic modulus of the present study (31.20 GPa). Due to the most accurate fit, the global relationship (R2 = 0.98, RMSE = 7.9, MAPE = 1.67) and the regional relationship (R2 = 0.96, RMSE = 5.24 MAPE = 0.91) were presented with very high accuracy between the dynamic and static modulus of elasticity. The results of artificial neural network and multivariate regression showed that estimation of shear wave velocity (Vs) based on P-wave velocity, water absorption and density is possible with high accuracy. The results showed that the ANN accuracy (R2 = 0.98, RMSE = 0.27) was higher than the multivariate linear regression (R2 = 0.86, RMSE = 0.39). The neural network also acts conservatively in predicting this variable.