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1.
Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer 总被引：1，自引：0，他引：1
As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency. 相似文献
2.
H. Sid Kalal Ph.D. H. Ahmad Panahi Ph.D. N. Framarzi E. Moniri Ph.D. A. Naeemy H. Hoveidi Ph.D. A. Abhari 《International Journal of Environmental Science and Technology》2011,8(3):501-512
A chelating resin is prepared by condensation polymerization of aniline with formaldehyde and characterized by Fourier transform infrared spectrometer, elemental analysis and thermogravimetric analysis and studied for the preconcentration and determination of trace Molybdate ion from environmental water sample using inductive couple plasma atomic emission spectroscopy. The optimum pH value for sorption of the metal ion was 5. The sorption capacity of functionalized resin is 3.1 mg/g. The chelating sorbent can be reused for 20 cycles of sorption-desorption without any significant change in sorption capacity. The best desorption of the metal ions from resin was obtained by 0.5 mol/L nitric acid as eluting agent. The profile of molybdenum uptake on this sorbent reflects good accessibility of the chelating sites in the aniline-formaldehyde. Scatchard analysis revealed that the homogeneous binding sites were formed in the polymers. The equilibrium adsorption data of Molybdate ion modified resin were analyzed by five isotherm models such as Langmuir, Freundlich and Temkin. Langmuir isotherm parameters obtained from the four Langmuir linear equations by using linear method. Based on the Langmuir isotherm analysis, the monolayer adsorption capacity was determined to be 4.03 mg/g at 20 °C. The method was applied for molybdenum ions determination from river water sample. 相似文献
3.
Achim Kopf Sylvia Stegmann Georg Delisle Behrouz Panahi Chingiz S. Aliyev Ibrahim Guliyev 《Marine and Petroleum Geology》2009
Active mud volcanism is a global phenomenon that represents a natural hazard by self-igniting eruptions and the continuous emission of methane gas in both marine and continental settings. Mud domes are often found in compressional tectonic settings such as the Caucasus orogenic wedge. Dashgil mud volcano, the most prominent of >200 features in Azerbaijan, has erupted vigorously in historic times. For several years, we have observed variations in the activity of Dashgil dome, including transients in methane flux, build-up of extrusive mud cones on the main feature, and flexural polygonal cracks adjacent to the main crater lake and new mud cones. In spring 2007, we carried out in situ CPTU (Cone Penetration Testing with Pore Pressure measurement) experiments in the crestal area of Dashgil. Our data suggest that the central portion of the crater lake, which hosts the conduit for gas (and possible mud) ascent, shows both low sediment shear strength (<5–20 kPa) and excess pore fluid pressures between 15 and 30 kPa supra-hydrostatic at 1 m sub-bottom depth. In situ cone resistance as a measure for undrained shear strength is as low as 150 kPa in the conduit, whereas the mud is found rather stiff in all other testing locations (300–700 kPa, probably a result of deeply buried shales of the Maikop formation parts of which now liquefy and ascend). Pore pressure is low in the centre of the conduit, probably because of rapidly migrating gas. It increases to 30 kPa at the lake bottom and deep flank, then decreases upslope on the lake flank, and reaches hydrostatic values at the crater rim. From the overpressured region beneath the fluid-filled crest of Dashgil dome, combined with the other observations, we suspect to currently witness an ongoing period of updoming. The presence of sintered mudstones from explosive eruptions in 1908 and 1928 (and most likely before) suggests that a similar violent activity may occur in the near future. 相似文献
4.
Phuong Thao Thi Ngo Mahdi Panahi Khabat Khosravi Omid Ghorbanzadeh Narges Kariminejad Artemi Cerda Saro Lee 《地学前缘(英文版)》2021,12(2):505-519
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies. 相似文献
5.
To quantify the spatial distribution of geochemical elements, the multifractality indices for Zn, Cu, Pt, Pd, Cr, Ni, Co, Pb, and As in lake-sediment samples in the Shining Tree area in the Abitibi area of Ontario are determined. The characterization of multifractal distribution patterns is based on the box-counting moment method and involves three functions: a mass exponent function (q); Coarse Hölder Exponent (q); and fractal dimension spectrum f( (q)). Properties of these functions at different values of q, characterize the spatial distribution of the variable under study. It is shown that the degree of multifractality defined by (1) can be used as a measure of irregularity of geochemical spatial dispersion patterns. The variations of Zn and Cu in the study area are characterized by relatively low degree of multifractality, whereas those for Pt, Pd, Cr, Ni, and Co; and particularly for As and Pb are characterized by higher multifractality indices.In the case of Zn and Cu, singularity spectra are close to a monofractal compared to the ones for As an Pb. The determination of multifractality indices allows us, in a quantitative way, to study the pattern of metal dispersions and link them to different physical processes, such as metal adsorption by organic material or glaciogenic processes. 相似文献
6.
Mohamadi Sedigheh Sammen Saad Sh. Panahi Fatemeh Ehteram Mohammad Kisi Ozgur Mosavi Amir Ahmed Ali Najah El-Shafie Ahmed Al-Ansari Nadhir 《Natural Hazards》2020,104(1):537-579
Natural Hazards - The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer... 相似文献
7.
Abdul-Lateef Balogun Fatemeh Rezaie Quoc Bao Pham Ljubomir Gigović Siniša Drobnjak Yusuf A. Aina Mahdi Panahi Shamsudeen Temitope Yekeen Saro Lee 《地学前缘(英文版)》2021,12(3):101104
In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance. 相似文献
8.
Mohammad Reza Panahi S. Morteza Mousavi Majid Rahimzadegan 《Environmental Earth Sciences》2017,76(23):792
Evaluation of groundwater resources in dry areas without enough data is a challenging task in many parts of the world, including Tehran–Karaj plain in Iran, which includes Tehran, the capital city of Iran and Karaj, one of Iran’s biggest cities. Water demand due to increasing agricultural and industrial activities caused many problems in the field of water resources management. In this study, the potential of groundwater resources was evaluated using remote sensing, geographic information system (GIS), and analytic hierarchy process (AHP) for the first time. Digital Elevation Model from Shuttle Radar Topography Mission was used to generate a slope map and drainage density map. Three Landsat-8 satellite images were utilized to provide lineament density and land cover/land use maps. Geological and soil type maps were provided from the Geological Survey and Mineral Explorations of Iran (GSI). Tropical Rainfall Measuring Mission data were used to prepare average annual precipitation map. Discharge values from 102 pumping wells in the time period of 2002–2014 were used to evaluate the results. Seven data layers were prepared, and the geodatabase was made in GIS. The layers and their classes were assigned weights using AHP method. Finally, the layers were overlaid based on their weights, and the potential map of groundwater resources was generated. The area was classified into five zones with very high, high, moderate, low, and very low potentials. The zones covered 5.95, 32.90, 22.70, 10.20, and 28.25% of the study area, respectively. The results showed good agreement with the field data obtained from discharge wells. 相似文献
9.
A numerical simulation algorithm based on the finite volume discretisation is presented for analyzing ship motions. The algorithm employs a fractional step method to deal with the coupling between the pressure and velocity fields. The free surface capturing is fulfilled by using a volume of fluid method in which the interface between the liquid (water) and gas (air) phases are computed by solving a scalar transport equation for the volume fraction of the liquid phase. The computed velocity field is employed to evaluate the acting forces and moments on the vessel. Using the strategy of boundary-fitted body-attached mesh and calculating all six degrees-of-freedom of motion in each time step, time history of ship motions including displacements, velocities and accelerations are evaluated.To verify the proposed algorithm, a series of verification tests are conducted. First, a two-dimensional asymmetrical wedge slamming is simulated as a simple type of a common case for high-speed vessels. Then, the steady-state forward motion of a high-speed planing catamaran is investigated. Results of both test cases show good agreement with experimental data. It is concluded that the proposed algorithm can be a promising strategy for both performance prediction and design of high-speed vessels. 相似文献
10.
H. Sid kalal M. Taghiof H. Hoveidi N. Pakizvand H. Vahidi H. A. Panahi S. Tavangari 《International Journal of Environmental Science and Technology》2013,10(5):1091-1102
In this study, the imprinted aniline–formaldehyde was used as an adsorbent for removal of Iridium and Palladium ions from aqueous solutions through batch equilibrium. The sorbent was characterized by fourier transform infrared spectroscopy. The influence of pH, equilibrium time, temperature and initial concentration of metal ions on adsorbed amount of both ions were investigated. The maximum adsorption capacity in initial concentration of 100 mg/L was found to be 12.5 mg/g at pH 7.0 and 14.3 mg/g at pH 8.0 for Iridium and Palladium, respectively. In addition, the best desorption of the metal ions from resin was obtained by 0.5 mol/L nitric acid as eluting agent. The profile of both ions uptake on this sorbent reflects good accessibility of the chelating sites in the imprinted aniline–formaldehyde. Langmuir, Freundlich, Temkin and Redlich–Peterson isotherm models were applied to analyze the experimental data. Moreover, Langmuir linear method was used to obtain the isotherm parameters. However, Langmuir type II achieved the highest coefficient which led to the best fit for the palladium and the best fit for Iridium obtained from linear Redlich–Peterson. However, the thermodynamic parameters (ΔG°, ΔH°, and ΔS°) were also determined using the equilibrium constant values obtained at different temperatures. The results showed that the adsorption for Iridium and Palladium ions was spontaneous nature and endothermic. Moreover, the method was applied for the determination of both ions from tap water samples. 相似文献