The effect of different meteorological parameters on the temporal variations of reference evapotranspiration

Temporal changes of meteorological variables can affect reference evapotranspiration (ET₀). The goal of the present research is to analyze the changes of ET₀ and identify the impact of effective meteorological parameters to the changes of ET₀. For this purpose, daily meteorological data recorded in 30 synoptic stations of Iran during 1960–2014 were used. The annual and seasonal values of ET₀ were calculated by the recorded data. To calculate ET₀, FAO56 Penman–Monteith method (standard method) was used. The annual and seasonal trends of ET₀ and its eight effective parameters were analyzed. Then the contributions of effective parameters changes on ET₀ were determined. To analyze ET₀ trend at annual and seasonal scales, two common methods, Spearman’s Rho and Mann–Kendall tests, were used. The R ² = 0.99 showed that the results of the mentioned methods were similar and on the basis of T-statistic <0.057, their difference was not significant (95% confidence level). Therefore, only one method’s results (Spearman’s Rho) were reported. On the basis of Spearman’s Rho results, the annual and seasonal values of ET₀ had negative trend in most of arid and semi-arid stations while the trend of this parameter was positive in humid and very humid stations. At annual and seasonal scales, decreasing in wind speed (W), temperature (T), sunshine hours (n), minimum temperature (TN), dew point temperature (TD), maximum temperature (TX), saturation vapor pressure deficit (SVPD) and solar radiation (RS) was observed in 58, 54, 39, 43, 56, 65, 65 and 37% studied stations, respectively. In many scales, the results showed that TX and W were the most effective meteorological variables on ET₀ changes and then SVPD was located in second step in arid and semi-arid stations. In humid and very humid stations, W was the first effective parameter at all scales, except autumn.     

Sequence signal processing of 2012 Ahar–Varzaghan earthquake (<Emphasis Type="Italic">M</Emphasis><Subscript>W</Subscript> 6.4) of NW Iran

Before starting seismic cycle of Ahar–Varzaghan 2012 event, a partial gap in the form of a pre-seismic calm sequence (seismicity rate, r = 0.46 event/year, b = 1.4) with duration of 303 days spatially has dominated over the entire seismogenic area. From April 17, 2012, to May 31, 2012, r significantly increased to 2.16, indicating strong foreshock sequence, and b value changed to 1.9, remarkably. In the last two months before the mainshock, foreshocks have partially migrated toward the earthquake fault (with a decrease in size, b = 2.0). Significantly, high rate of seismicity and low V _P /V _S (1.64) in the foreshocks sequence and also very high seismicity rate (17.3) and high V _P /V _S (1.76) in the aftershocks sequence make substantial differences between the seismic cycle and the background seismicity. Moreover, a significant E–W migration of the microseismicity was confirmed in the study area.     

Landfill site selection using combination of fuzzy logic and multi-attribute decision-making approach

During the last decades, growth of urbanization and industrialization led to an increase in solid waste generation. Landfilling is the most prevalent ultimate disposal method for the municipal solid wastes in developing countries. The rapid municipal solid waste generation in Markazi province (central part of Iran) causes the need for precision in finding a suitable landfill site selection. In the present study, 12 factors (environmental and socioeconomic factors) have been applied to select the landfill site in Markazi province, Iran. The different methods including the analytic network process (ANP) combined with fuzzy linguistic quantifier, ordered weighted average (OWA), and weighted linear combination (WLC) approach in geographic information system was applied to find an appropriate landfill site. The OWA operator function permits the evaluation of the wide spectrum of consequences (with different scenario) obtained from different management strategies. Results revealed that integration of fuzzy logic, ANP, and OWA provides flexible and better ideas compared to the Boolean logic and WLC to select a suitable landfill site.     

Denoising and improving the quality of seismic data using combination of DBM filter and FX deconvolution

Seismic data denoising, random noise attenuation (RNA) and spike-like noise suppression, is a main consideration for improving the quality of records. RNA could increase signal to noise ratio (S/N) to avoid misinterpretation of seismic data. In this research, a novel method is created by using the combination of frequency-offset deconvolution (FXD) and decision-based median (DBM) filter for RNA from seismic data. The method is applied in two main phases; FXD is focused to remove the Gaussian noise and DBM filter is focused to attenuate the impulsive noise and spikes. To implement and verify the method, three types of data are used: two synthetic models (a model with linear events and a model with hyperbolic events) and an observed seismic section. The ability of the proposed method (FXD-DBM) in comparison of applying each in seismic RNA application is proven. The noise level is reduced obviously, and hence, the S/N of all examined seismic records is increased considerably after denoising by the combination of FX deconvolution and DBM filter. About the real seismic section, suppressing random noise and spikes show up improving the seismic reflector continuity and hence enhancing the interpretability of data. Moreover, some masked events by random noise are clarified in different parts of data after denoising using the planned method.     

Interpretation of magnetic and gravity gradient tensor data using normalized source strength – A case study from McFaulds Lake,Northern Ontario,Canada

In this paper, we present a case study on the use of the normalized source strength (NSS) for interpretation of magnetic and gravity gradient tensors data. This application arises in exploration of nickel, copper and platinum group element (Ni‐Cu‐PGE) deposits in the McFaulds Lake area, Northern Ontario, Canada. In this study, we have used the normalized source strength function derived from recent high resolution aeromagnetic and gravity gradiometry data for locating geological bodies. In our algorithm, we use maxima of the normalized source strength for estimating the horizontal location of the causative body. Then we estimate depth to the source and structural index at that point using the ratio between the normalized source strength and its vertical derivative calculated at two levels; the measurement level and a height h above the measurement level. To discriminate more reliable solutions from spurious ones, we reject solutions with unreasonable estimated structural indices. This method uses an upward continuation filter which reduces the effect of high frequency noise. In the magnetic case, the advantage is that, in general, the normalized magnetic source strength is relatively insensitive to magnetization direction, thus it provides more reliable information than standard techniques when geologic bodies carry remanent magnetization. For dipping gravity sources, the calculated normalized source strength yields a reliable estimate of the source location by peaking right above the top surface. Application of the method on aeromagnetic and gravity gradient tensor data sets from McFaulds Lake area indicates that most of the gravity and magnetic sources are located just beneath a 20 m thick (on average) overburden and delineated magnetic and gravity sources which can be probably approximated by geological contacts and thin dikes, come up to the overburden.     

Application of the Ordered Weighted Averaging (OWA) method to the Caspian Sea conflict

This study proposes a promising allocation mechanism of the Caspian Sea natural resources, which are presently shared among five countries. To date, these nations have been unable to reach an allocation agreement. We apply a methodology to propose the most appropriate solution under different risk attitudes of the states. This research is different from other studies regarding the Caspian Sea negotiations in that it employs risk-based fuzzy multi attribute decision making methods for simulating the risk attitudes or optimism/pessimism degrees of the decision makers. The ordered weighted averaging (OWA) approach, which considers the optimism/pessimism degree quantitatively, is used to take into account the effects of different risk attitudes of the negotiators on the final outcome. We demonstrate how one could obtain a range of alternatives under different multi attribute and risk attitudes. The induced OWA (IOWA) method is also used to determine the relative power of these states bordering the Caspian Sea by considering several attributes, including different risk attitudes of agents. Results indicate that taking into account the risk attitude (prone, neutral, averse) of the states can affect the overall ranking of the proposed solutions. The findings from this study may facilitate negotiation regarding the most preferred allocation mechanism for the Caspian Sea.     

Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin

A total dissolved solid (TDS) is an important indicator for water quality assessment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationship of mineral salt composition with TDS. In the present study, four artificial intelligence approaches, namely artificial neural networks (ANNs), two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast TDS in river water over a period of 18 years at seven different sites. Five different GEP, ANFIS and ANN models comprising various combinations of water quality and flow variables from Zarinehroud basin in northwest of Iran were developed to forecast TDS variations. The correlation coefficient (R), root mean square error and mean absolute error statistics were used for evaluating the accuracy of models. Based on the comparisons, it was found that the GEP, ANFIS-GP, ANFIS-SC and ANN models could be employed successfully in forecasting TDS variations. A comparison was made between these artificial intelligence approaches which emphasized the superiority of GEP over the other intelligent models.     

Centrifuge modeling of buried continuous pipelines subjected to normal faulting

Seismic ground faulting is the greatest hazard for continuous buried pipelines.Over the years,researchers have attempted to understand pipeline behavior mostly via numerical modeling such as the finite element method.The lack of well-documented field case histories of pipeline failure from seismic ground faulting and the cost and complicated facilities needed for full-scale experimental simulation mean that a centrifuge-based method to determine the behavior of pipelines subjected to faulting is best to verify numerical approaches.This paper presents results from three centrifuge tests designed to investigate continuous buried steel pipeline behavior subjected to normal faulting.The experimental setup and procedure are described and the recorded axial and bending strains induced in a pipeline are presented and compared to those obtained via analytical methods.The influence of factors such as faulting offset,burial depth and pipe diameter on the axial and bending strains of pipes and on ground soil failure and pipeline deformation patterns are also investigated.Finally,the tensile rupture of a pipeline due to normal faulting is investigated.     

Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain,Iran

ABSTRACT

Groundwater-level time series often have a substantial number of missing values which should be taken into consideration before using them for further analysis, particularly for numerical groundwater flow modelling applications. This study aims to comprehensively compare two data-driven models, singular spectrum analysis (SSA) and multichannel spectrum analysis (MSSA), to reconstruct groundwater-level time series and impute the missing values for 25 piezometric stations in Ardabil Plain, northwest Iran. The reconstructed groundwater-level time series are assessed against the complete observed groundwater time series, while the imputed values are appraised against the artificially created gap values. The results show that both SSA and MSSA demonstrate a solid competency in imputation and reconstruction of groundwater-level data. However, depending on the spatial correlation between the piezometers, and the most suitable probability distribution function (pdf) fitted to the time series of each piezometer, the performance may vary from piezometer to piezometer.