Weathering and soils formation on different parent materials in Golestan Province,Northern Iran     

Maryam Mahmoodi  Farhad Khormali  Arash Amini  Shamsollah Ayoubi 《山地科学学报》2016,13(5):870-881

Geochemical, mineralogical, and micromorphological characteristics of soils and their relevant parent rocks including loess, ignimbrite, sandstone and limestone were investigated to identify the soil-parent material uniformity and the weathering degree of soils in Golestan Province, northern Iran. Highly developed Calcixerolls and moderately developed Haploxerepts were formed on loess and limestone, respectively. In contrast, the soils formed on ignimbrite and sandstone were non-developed Entisols. Illite was the dominant clay mineral found in ignimbrite and sandstone in both the A horizon and parent material. In loess derived soils however, smectite was dominant especially in the Bt horizon compared to its parent material indicating partly to its pedogenic formation. In limestone, illite and vermiculite were dominant both in the A and C horizons. Ti/Zr ratio proved that the studied soils were closely related to their underlying parent materials geochemically. Chemical index of alteration (CIA), micromorphological index of soil development (MISECA), smectite/illite+chlorite ratio and magnetic susceptibility were applied to investigate the degree of soil development. Results showed that the most and the least developed soils were those formed on loess deposits and limestone, respectively. Application of the different geochemical and pedogenetic approaches was proved to be useful in identifying the relevance of soils to their underlying parent materials and also their degree of development.  相似文献   

Reliability and sensitivity analysis of wedge stability in the abutments of an arch dam using artificial neural network     

Mostafaei  Hasan  Behnamfar  Farhad  Alembagheri  Mohammad 《地震工程与工程振动(英文版)》2022,21(4):1019-1033

In this study, the seismic stability of arch dam abutments is investigated within the framework of the probabilistic method. A large concrete arch dam is considered with six wedges for each abutment. The seismic safety of the dam abutments is studied with quasi-static analysis for different hazard levels. The Londe limit equilibrium method is utilized to calculate the stability of the wedges in the abutments. Since the finite element method is time-consuming, the neural network is used as an alternative for calculating the wedge safety factor. For training the neural network, 1000 random samples are generated and the dam response is calculated. The direction of applied acceleration is changed within 5-degree intervals to reveal the critical direction corresponding to the minimum safety factor. The Latin hypercube sampling (LHS) is employed for sample generation, and the safety level is determined with reliability analysis. Three sample numbers of 1000, 2000 and 4000 are used to examine the average and standard deviation of the results. The global sensitivity analysis is used to identify the effects of random variables on the abutment stability. It is shown that friction, cohesion and uplift pressure have the most significant effects on the wedge stability variance.

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Allocative efficiency analysis of wheat and cotton in district Khanewal,Punjab, Pakistan     

Zulfiqar  Farah  Shang  Jie  Nasrullah  Muhammad  Rizwanullah  Muhammad 《GeoJournal》2021,86(6):2777-2786

GeoJournal - To compete in the international market, a farmer in Pakistan needs to decrease its production cost. Agricultural researchers and policymakers are focusing on increasing the...  相似文献   

Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach     

Farhad Alizadeh  Gharamaleki  Alireza Faregh  Jalilzadeh  Mojtaba  Akhoundzadeh  Ali 《Water Resources》2020,47(1):41-53

Water Resources - This study proposed a hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble...  相似文献   

A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique     

Kiyoumars Roushangar  Farhad Alizadeh 《山地科学学报》2018,15(7):1481-1497

Determination of homogenous precipitation-based regions is a very important task in effective management of water resources. The present study tried to propose an effective precipitation-based regionalization methodology by conjugating both temporal pre-processing and spatial clustering approaches in a way to take advantage of multiscale properties of precipitation time series. Annual precipitation data of 51 years (1960-2010) for 31 rain gauges (RGs) were collected and used in proposed clustering approaches. Discreet wavelet transform (DWT) was used to capture the time-frequency attributes of the time series and multiscale regionalization was performed by using k-means and Self Organizing Maps (SOM) clustering techniques. Daubechies function (db) was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the approximation (A) and detail (D) coefficients were used to determine the input dataset as a basis of spatial clustering. The proposed model’s efficiency in spatial clustering stage was verified using three different indexes namely, Silhouette Coefficient (SC), Dunn index and Davis Bouldin index (DB). Results approved superior performance of k-means technique in comparison to SOM. It was also deduced that DWT-based regionalization methodology showed improvements in comparison to historical-based models. Cross mutual information was used to investigate the RGs of cluster 3’s homogeneousness in DWT-k-means approach. Results of non-linear correlation approach verified homogeneity of cluster 3. Verifications based on mean annual precipitation values of rain gauges in each cluster also approved the capability of multiscale approach in precipitation regionalization.  相似文献   

Finite Element Modeling of Extensional Structures in the Annapurna Region of Central Himalayas     

M. Farhad Howladar 《Geotechnical and Geological Engineering》2009,27(5):607-617

Two-dimensional, elastic, plane-strain, finite element model are generated to investigate the extensional structures mainly normal fault in the Annapurna region, central Himalaya. The numerical study was performed on the Miocene geologic profile considering both of the convergent displacement and rock layer properties in the regime. Results show that the normal fault primarily influenced by model geometry, rheology (layer properties) and boundary condition (applied convergence displacement). Simulated normal fault density exhibits very high intensity in Lesser Himalaya then in the Tethys Himalaya and low intensity in the Higher Himalaya, suggesting the vulnerability of fault development to low-grade metamorphic rock than the high-grade rocks. The location of normal fault predicted by the numerical model analysis is consistent to the position of normal fault segments by Kaneko (J Geol Soc Jpn 103(3):203–226, 1997). In this studies, it also believed that the presence of these normal faults and underthrusting of the sub-Himalayan sequence with associated tectonic forces, the Himalayan Metamorphic belt has been exhumed and differentially domal uplifted and then segmented into several blocks.  相似文献   

Analytical model of strange star in low-mass X-ray binary KS 1731-260     

Sk. Monowar Hossein  Nur Farhad  Sajahan Molla  Mehedi Kalam 《Astrophysics and Space Science》2016,361(10):333

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Geochemistry of the kaolin deposits of Swat (Pakistan)     

Muhammad Ashraf Siddiqui  Zulfiqar Ahmed   《Chemie der Erde / Geochemistry》2008,68(2):207-219

Kaolin deposits of the Swat District in Pakistan are indicated to have derived by hydrothermal alteration of more feldspathic parts of felsic intrusives, which occur enclosed in orthoamphibolites and orthogneisses of the Cretaceous Kohistan Island Arc terrane. These latter “country rocks” formed under epidote–amphibolite conditions that prograde northwards to amphibolite facies, and locally manifest slight metamorphic differentiation. The felsic intrusives exhibit a general decrease in siliceous character from west to east, but are less siliceous than most hosts of world kaolins. They are composed of chemically allied quartz diorite, tonalite, trondhjemite and pegmatoids, which evolved mainly by an orthomagmatic crystal fractionation. These parental rocks are calc-alkaline in nature, and kaolinization has proceeded in Ca-richer environment. This is in variance with the occurrence of most known kaolin deposits over potassic granites or rhyolites. Ca-metasomatism of the “host rocks” is in evidence. Kaolin formation by a supergene process is not displayed.The raw kaolin with contained unaltered plagioclase is characterized by a rather low silica (46.54–50.93%) and potash (<1%), and high alumina (23.54–26.77%), Fe₂O₃ (1.73–5.45%) and lime (8.13–16.93%) content. Kaolinization proceeded with a decrease in SiO₂ and concomitant increase in Al₂O₃. The same trend is followed with fineness of grain size of washed fractions, in resemblance to other known kaolin deposits of primary as well as secondary origin.  相似文献   

A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data     

Behnaz Bigdeli  Farhad Samadzadegan  Peter Reinartz 《Journal of the Indian Society of Remote Sensing》2013,41(4):763-776

With recent technological advances in remote sensing sensors and systems, very high-dimensional hyperspectral data are available for a better discrimination among different complex land-cover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods.  相似文献   

Forecasting Daily Seepage Discharge of an Earth Dam Using Wavelet–Mutual Information–Gaussian Process Regression Approaches     

Kiyoumars Roushangar  Saeede Garekhani  Farhad Alizadeh 《Geotechnical and Geological Engineering》2016,34(5):1313-1326

Because of their sensitive structure, earth dams might face failure due to seepage phenomenon. In order to prevent such failure, some equipment like piezometers are installed in the body or foundation of earth dams. This study investigated the importance of piezometer installation level in dam body or foundation using mutual information–wavelet–Gaussian process regression. 27 Piezometers in three section along with reservoir level were employed to predict one-step-ahead seepage discharge of Zonouz earth dam. The daily data of 1 year of piezometer level and reservoir level were collected for this purpose. In order to find the best possible input combination, three groups of modeling scenarios were defined using piezometers and reservoir level time series. As some input combinations had more than two variables, decomposed time series were imposed into mutual information (MI) tool in order to decrement input variables and find the most correlated input–output features. Afterward, mentioned features were imposed into optimized Gaussian process regression (GPR) to be predicted. Different kernels were selected as core tool of GPR, but results demonstrated the capability of radial basis function (RBF) kernel. GPR–RBF structure were optimized using cross-validation technique. Results indicated that input combination including piezometer level and reservoir level of section II, especially piezometer 203 time series led to the best result among all scenarios.  相似文献