Geochemical Prospectivity Mapping Through a Feature Extraction–Selection Classification Scheme     

Zekri  Hamid  Cohen  David R.  Mokhtari  Ahmad Reza  Esmaeili  Abbas 《Natural Resources Research》2019,28(3):849-865

Natural Resources Research - Machine learning (ML) schemes can enhance success in geochemical prospectivity mapping. This study has examined the effectiveness of several feature extraction or...  相似文献   

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks          下载免费PDF全文

Hossein Sheikhian  Mahmoud Reza Delavar  Alfred Stein 《Transactions in GIS》2017,21(6):1237-1259

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi‐layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty‐five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R² = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.  相似文献   

Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran     

Zamani  Reza  Akhond-Ali  Ali-Mohammad  Roozbahani  Abbas  Fattahi  Rouhollah 《Theoretical and Applied Climatology》2017,129(3-4):1109-1121

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

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Standard thermodynamic data for Rhodochrosite from equilibrium decomposition curve     

Ali Reza Fazeli  J. A. K. Tareen  B. Basavalingu  G. T. Bhandage 《Journal of Earth System Science》1991,100(1):37-39

Hydrothermal equilibrium decomposition curve for MnCO₃⇌MnO + CO₂ in the total CO₂ pressure range of 100–1700 bars and temperature range of 500–800°C was studied. The standard thermodynamic data obtained are: ΔH⁰ _f= − 894.382 ± 0.74 kj/mol and ΔG⁰ _f = − 822.170 ± 0.74 kj/mol. These values are more negative than the reported calorimetric data.  相似文献   

A Geometric Computational Model for Calculation of Longwall Face Effect on Gate Roadways     

Hamid Mohammadi  Mohammad Ali Ebrahimi Farsangi  Hossein Jalalifar  Ali Reza Ahmadi 《Rock Mechanics and Rock Engineering》2016,49(1):303-314

In this paper a geometric computational model (GCM) has been developed for calculating the effect of longwall face on the extension of excavation-damaged zone (EDZ) above the gate roadways (main and tail gates), considering the advance longwall mining method. In this model, the stability of gate roadways are investigated based on loading effects due to EDZ and caving zone (CZ) above the longwall face, which can extend the EDZ size. The structure of GCM depends on four important factors: (1) geomechanical properties of hanging wall, (2) dip and thickness of coal seam, (3) CZ characteristics, and (4) pillar width. The investigations demonstrated that the extension of EDZ is a function of pillar width. Considering the effect of pillar width, new mathematical relationships were presented to calculate the face influence coefficient and characteristics of extended EDZ. Furthermore, taking GCM into account, a computational algorithm for stability analysis of gate roadways was suggested. Validation was carried out through instrumentation and monitoring results of a longwall face at Parvade-2 coal mine in Tabas, Iran, demonstrating good agreement between the new model and measured results. Finally, a sensitivity analysis was carried out on the effect of pillar width, bearing capacity of support system and coal seam dip.  相似文献   

An efficient artificial intelligence model for prediction of tropical storm surge   总被引：1，自引：0，他引：1  

M. Reza Hashemi  Malcolm L. Spaulding  Alex Shaw  Hamed Farhadi  Matt Lewis 《Natural Hazards》2016,80(1):471-486

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Regional modeling of rainfall-induced landslides using TRIGRS model by incorporating plant cover effects: case study in Hulu Kelang,Malaysia     

Nader Saadatkhah  Shattri Mansor  Azman Kassim  Lee Min Lee  Reza Saadatkhah  Ali Sobhanmanesh 《Environmental Earth Sciences》2016,75(5):445

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Geochemical evaluation and thermal modeling of the Eocene–Oligocene Pabdeh and Middle Cretaceous Gurpi Formations in the northern part of the Dezful Embayment     

Ahmad Reza Karimi  Ahmad Reza Rabbani  Mohammad Reza Kamali  Mohamad Hosein Heidarifard 《Arabian Journal of Geosciences》2016,9(5):423

Rock–Eval pyrolysis analysis, burial history, and 1D thermal maturity modeling have allowed the evaluation of the source rock potential, thermal maturation state, and impacts of the Pabdeh and Gurpi Formations in Cretaceous–Miocene petroleum system in the Naft Safid (NS) and Zeloi (ZE) oilfields, North Dezful Embayment. The total organic carbon (TOC) content of the Pabdeh and Gurpi Formations ranges from 0.2 to 4.7 wt% and 0.3 to 5.3 wt%, respectively. S2 values of the Pabdeh Formation in the ZE and NS oilfields vary from 0.41 to 13.77 and 0.29 to 14.5 mg HC (Hydrocarbon)/g rock, with an average value of 4.48 and 4.14 mg HC/g rock, respectively. These values for the Gurpi Formation in the ZE and NS oilfields range from 0.31 to 16.96 and 0.26 to 1.44 mg HC/g rock, with an average value of 8.54 and 2.43 mg HC/g rock, respectively. The S2 versus TOC diagram reveals a fair to good hydrocarbon generation potential of the Pabdeh Formation and poor to fair potential of the Gurpi Formation. The high values of S2 (S2 > S1) for samples of the both formations in the ZE and NS oilfields show that the samples are not contaminated with petroleum generated from underlying source rocks. The samples of the Pabdeh Formation in the ZE oilfield are characterized by a relatively narrow range of activation energy values with principal activation energy of 46 kcal/mol and frequency factor of 5.27 × 10⁺¹¹ s^?1. It seems that the high sulfur content of the Pabdeh organic matter probably caused the frequency factor and principal activation energy to be lower than usual. Hydrogen index (HI) values of the Pabdeh and Gurpi Formations in the ZE oilfield vary from 71 to 786 and 97 to 398 mg HC/g TOC, with an average value of 310 and 277 mg HC/g TOC, respectively. These values in the NS oilfield range from 66 to 546 and 51 to 525 mg HC/g TOC, with an average value of 256 and 227 mg HC/g TOC, respectively. Plot of HI vs. T _max value indicates that the majority of the Pabdeh and Gurpi samples contain predominantly type II kerogen and their organofacies are directly related to the more homogeneous precursor materials. Based on thermal maturity modeling results, kinetic parameters, and Rock–Eval analysis, both formations in the ZE and NS oilfields are thermally mature and immature or early mature stage, respectively.  相似文献   

Determination of sand dune characteristics through geomorphometry and wind data analysis in central Iran (Kashan Erg)   总被引：1，自引：0，他引：1  

Aliakbar Nazari Samani  Hassan Khosravi  Tayyebeh Mesbahzadeh  Maryam Azarakhshi  Mohammad Reza Rahdari 《Arabian Journal of Geosciences》2016,9(18):716

The combination of wind measurements and remotely sensed geomorphometry indices provides a valuable resource in the study of desert landforms, because arduous desert environments are difficult to access. In this research, we couple wind data and geomorphometry to separate and classify different sand dunes in Kashan Erg in central Iran. Additionally, the effect of sand-fixing projects on sand dune morphology was assessed using geomorphometry indices (roughness, curvature, surface area, dune spacing and dune height). Results showed that a Digital Elevation Model of the National Cartographic Center of Iran (NCC DEM) with 10-m resolution and accuracy of 54% could discriminate geomorphometry parameters better than the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 30-m resolution and Shuttle Radar Topography Mission (SRTM) data with 90-m resolution and 45.2 and 1.6% accuracy, respectively. Low classification of SRTM DEM was associated with too many non-value points found in the DEM. Accuracy assessment of comparison ground control points revealed that ASTER DEM (RMSE = 4.25) has higher accuracy than SRTM and NCC DEMs in this region. Study of curvature showed that transverse and linear sand dunes were formed in concave topography rather than convex. Reduced slopes in fixed sand dunes were established due to wind erosion control projects. Measurements of dune height and spacing show that there is significant correlation in compound dunes (R ² = 0.546), linear dunes (R ² = 0.228) and fixed dunes (R ² = 0.129). In general, the height of dunes in Kashan Erg increases from the margin of the field to the center of the field with a maximum height of 120 m in star dunes. Analysis of wind data showed that sand drift potential is in low-medium class in Kashan Erg. Linear sand dunes in Kashan Erg show that they are following a global trend in forming of these. Finally, established of geomorphometry method in dune classification will help researchers to identify priority of land management and performance assessment of sand dunes fixing projects in arid arduous environment.  相似文献   

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study     

Najmeh?Khalili  Saeed?Reza?KhodashenasEmail author  Kamran?Davary  Mohammad?Mousavi?Baygi  Fatemeh?Karimaldini 《Arabian Journal of Geosciences》2016,9(13):624

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.  相似文献