In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping. 相似文献
In the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects. 相似文献
The Mendejin pluton is located in the Mianeh area, NW Iran, 550 km from Tehran. This pluton is probably of Oligo-Miocene age and is the result of extensive magmatism which occurred during and after the Alpine Orogeny. Similar plutons are common in the Alborz–Azarbaijan structural zone of Iran, and it is likely that there are concealed plutons related to this extensive Cenozoic magmatism, but due to their youth and low rates of erosion they have not yet been exposed. The Mendejin pluton is a composite body made up of four types of plutonic rocks: pink tonalite, grey tonalite, diorite and aplite. The pink tonalite is porphyritic and contains phenocrysts of plagioclase, K-feldspar and hornblende in a groundmass consisting of quartz, plagioclase, K-feldspar, hornblende, zircon, monazite, leucoxene, apatite and hematite. The grey porphyritic tonalite has more biotite, pyroxene and pyrite and less accessory phases compared with the pink tonalite. The diorite has a microporphyritic texture with phenocrysts of plagioclase, hornblende and augite. This rock also occurs as xenoliths in the Mendejin pluton. The aplitic dykes are the youngest magmatic products at Mendejin. The Mendejin tonalite contains more Cl, As, S, Cu, Ni and Zn than the global granite. These rocks are of I-type, peraluminous and calc-alkaline, with medium to high potassium, and were formed as part of a volcanic arc. The Mendejin pluton contains up to 8 ppb gold and could potentially have been the source of an economic gold deposit by leaching of Au from wall rocks and deposition in extensive hydrothermally altered marginal zones. 相似文献
AbstractMuch of the prairie region in North America is characterized by relatively flat terrain with many depressions on the landscape. The hydrological response (runoff) is a combination of the conventional runoff from the contributing areas and the occasional overflow from the non-contributing areas (depressions). In this study, we promote the use of a hybrid modelling structure to predict runoff generation from prairie landscapes. More specifically, the Soil and Water Assessment Tool (SWAT) is fused with artificial neural networks (ANNs), so that SWAT and the ANN module deal with the contributing and non-contributing areas, respectively. A detailed experimental study is performed to select the best set of inputs, training algorithms and hidden neurons. The results obtained in this study suggest that the fusion of process-based and data-driven models can provide improved modelling capabilities for representing the highly nonlinear nature of the hydrological processes in prairie landscapes.
Editor D. Koutsoyiannis; Associate editor L. See 相似文献
Flyrock is one of the most important environmental issues in mine blasting, which can affect equipment, people and could cause fatal accidents. Therefore, minimization of this environmental issue of blasting must be considered as the ultimate objective of many rock removal projects. This paper describes a new minimization procedure of flyrock using intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization (PSO) algorithms. The most effective factors of flyrock were used as model inputs while the output of the system was set as flyrock distance. In the initial stage, an ANN model was constructed and proposed with high degree of accuracy. Then, two different strategies according to ideal and engineering condition designs were considered and implemented using PSO algorithm. The two main parameters of PSO algorithm for optimal design were obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to 109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly minimized and controlled.
This research addressed the separate and combined impacts of climate and land use change on streamflow, suspended sediment and water quality in the Kor River Basin, Southwest of Iran, using (BASINS–WinHSPF) model. The model was calibrated and validated for hydrology, sediment and water quality for the period 2003–2012. The model was run under two climate changes, two land use changes and four combined change scenarios for near-future period (2020–2049). The results revealed that projected climate change impacts include an increase in streamflow (maximum increases of 52% under RCP 2.6 in December and 170% under RCP 8.5). Projected sediment concentrations under climate change scenarios showed a monthly average decrease of 10%. For land use change scenarios, agricultural development scenario indicated an opposite direction of changes in orthophosphate (increases in all months with an average increase of 6% under agricultural development scenario), leading to the conclusion that land use change is the dominant factor in nutrient concentration changes. Combined impacts results indicated that streamflows in late fall and winter months increased while in summer and early fall decreased. Suspended sediment and orthophosphate concentrations were decreased in all months except for increases in suspended sediment concentrations in September and October and orthophosphate concentrations in late winter and early spring due to the impact of land use change scenarios. 相似文献
The Gol-e-Zard Zn-Pb deposit is one of several sediment-hosted Zn-Pb deposits found in the central part of the Sanadaj-Sirjan Zone, known as the Isfahan-Malayer belt, western Iran. Mineralization occurs in Upper Triassic to Jurassic phyllites and meta-sandstones. Sphalerite and galena are the most abundant metallic ores, with minor chalcopyrite. Calcite and quartz are the main gangue minerals. Fissure filling, replacement textures and especially mineralized faults, suggest an epigenetic stage in the Gol-e-Zard deposit formation. Geochemical studies of mineralized rocks show high concentrations of Zn, Pb and Cu, (Zn and Pb > 10000 ppm and Cu average 3000 ppm). LREE enrichment (LREE>HREE, La/Lu average 1.44) and positive Eu anomalies (Eu/Eu*>1 average 1.67) indicate reducing conditions during the deposition of deposit. However, some samples do not display negative Ce anomalies, which indicate that localized oxidizing conditions are also present. This study indicates that the Gol-e-Zard deposit formed due to circulating hydrothermal fluids in a marine environment. A SEDEX-type genesis, which is defined by circulating hydrothermal fluids through sediments in a marine environment, and syngenetic precipitation of Zn and Pb sulphides, is suggested for the Gol-e-Zard deposit. Emplacement of some granitoid intrusions such as the Aligudarz granitoid intrusion remobilized mineralizing fluids and metamorphosed the Gol-e-Zard deposit. 相似文献
Characterization of karst systems and forecast of their state variables are essential for groundwater management and engineering in karst regions. These objectives can be met by the use of process-based discrete-continuum models (DCMs). However, results of DCMs may suffer from inversion nonuniqueness. It has been demonstrated that the joint inversion of observations regulated by different natural processes can tackle the nonuniqueness issue in groundwater modeling. However, this has not been tested for DCMs thus far. This research proposes a methodology for the joint inversion of hydro-thermo-chemo-graphs, applying to two small-scale sink-to-spring experiments at Freiheit Spring, Minnesota, USA. In order to address conceptual uncertainty, a multimodel approach was implemented, featuring seven mutually exclusive variants. Spring hydro-thermo-chemo-graphs, for all the variants simulated by MODFLOW-CFPv2, were jointly inverted using a weighted least squares algorithm. Subsequently, models were compared in terms of inversion and forecast performances, as well as parameter uncertainties. Results reveal the suitability of the DCM approach for simultaneous inversion and forecast of hydro-physico-chemical behavior of karst systems, even at a scale of meters and seconds. The estimated volume of the tracer conduit passage ranges from approximately 46–51 m3, which is comparable to the estimate from the flood-pulse method. Moreover, it was demonstrated that the thermograph and hydrograph contain more information about aquifer characteristics than the chemograph. However, this finding can be site-specific and should depend on the analysis scale, the considered conceptual models, and the hydrological state, which are potentially affected by minor unaccountable processes and features.
