Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran

Natural Hazards - Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk...     

Automatic calibration tool for river models based on the MHYSER software

Due to their complex nature, river models require extensive calibration in order to achieve reliable model predictions. Manually fitting the numerous parameters included in this procedure can be a laborious and repetitive process. This paper presents a new instrument, developed specifically for the automatic calibration of river models based on the software MHYSER. The instrument is completely autonomous and returns the model with the parameter values giving rise to the smallest difference between the model-generated observations and the measured observations. It utilises the software PEST to fit continuous calibration parameters and exceeds the program’s capabilities in order to also fit discontinuous calibration parameters. Testing of the instrument is accomplished using three models, one of which was developed during a study on the dynamics of sediments on the Romaine River, situated in the Eastern region of the Province of Quebec.     

Analysis of observed and projected interannual variability in the summer season onset,length, and end dates across the Iran

Theoretical and Applied Climatology - It is very important to study the role of global warming on the variability of summer characteristics in arid and semi-arid climates such as Iran, because of...     

Prediction of Enhanced Soil–Anchored Geogrid Interactions in Direct Shear Mode Using Gene Expression Programming

Design of reinforced soil structures is greatly influenced by soil–geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil–anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil–geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the Gene Expression Programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil–anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (σ_n) and anchorage length (L) and the least effective parameters are average particle size (D₅₀) and anchor base area (A_b).

     

Crustal structure of Iraq from receiver functions and surface wave dispersion: implications for understanding the deformation history of the Arabian–Eurasian collision

We report the crustal structure for two locations in Iraq estimated by joint inversion of P -wave receiver functions (RFs) and surface (Rayleigh) wave group velocity dispersion. RFs were computed from teleseismic recordings at two temporary broad-band seismic stations located in Mosul (MSL) in the Zagros Fold Belt and Baghdad (BHD) in the Mesopotamian Foredeep. Group velocity dispersion curves at the sites were derived from continental-scale tomography. The inversion results show that the crustal thicknesses are 39 km at MSL and 43 km at BHD. We observe a strong Ps _Moho at BHD consistent with a sharp Moho discontinuity. However, at MSL we observe a weak Ps _Moho suggesting a transitional Moho where crustal thickening is likely to be occurring in the deep crust. Both sites reveal low velocity surface layers consistent with sedimentary thickness of about 3 km at station MSL and 7 km at BHD and agreeing well with the previous reports. Ignoring the sediments, the crystalline crustal velocities and thicknesses are remarkably similar at both stations. The similarity of crustal structure suggests that the crust of the northeastern proto-Arabian Platform was uniform before subsidence and deposition of the sediments in the Cenozoic. If crystalline crustal structure is uniform across the northern Arabian Platform then crustal thickness variations in the Zagros Fold Belt and Thrust Zone should reveal the history of deformation and crustal shortening in the Arabian–Eurasian collision zone and not reflect pre-existing crustal thickness variations in the Arabian Plate.     

Determination of failure probabilities of flood defence systems with improved dynamic bounds method

In this paper, we describe the computational framework of a novel method for solving the challenging problem of probabilistic finite elements. The method is called Improved Dynamic Bounds (IDB) and was developed recently to improve the efficiency of the dynamic bounds. The IDB is used in finite element numerical models to calculate time-dependent failure analyses of structures. In applications, the IDB can speed up the overall simulation process by several orders of magnitude. In applications controlled by two influential variables (e.g, two-dimensional problem), the computational efficiency is improved by a factor of 769 according to Rajabalinejad (2009). Applications of IDB indicate the method is most efficient for problems where the number of influential variables are limited. This is often the case for geotechnical and coastal flood defence systems. The IDB method is applied in this paper to the 17th Street Flood Wall, a component of the flood defence system (levee infra-structure) that failed during the Hurricane Katrina, to calculate the failure probability of an I-wall.     

Context-aware abstraction and generalization of street networks: two cognitively engineered user-oriented approaches using network Voronoi diagrams

Generalization and spatial contextual awareness are prevalent concepts in geographic information systems. This study adopted a context-dependent user-centred network generalization method to create a sub-network for optimal route finding. The results show an increase in the number of preferred traversed edges. A route presentation approach is suggested that is based on cognitively engineered user-oriented abstraction of street network. Successive abstractions hierarchize the street network to create a hierarchical presentation structure. The route is projected onto one level in the structure. Then, instead of showing the exact route, the network Voronoi regions represented by the projected route are shown. Experience indicates that the suggested method is an efficient way of route presentation for a hierarchical regionalized structure of a human cognitive map.     

Discussion of “Consequences of dike breaches and dike overflow in a bifurcating river system” by Anouk Bomers,Ralph M. J. Schielen and Suzanne J. M. H. Hulscher

In this discussion, the authors will point out that even if Bomers et al. (Nat Hazards 97:309–334, 2019) tackle an important problem, ignoring the uncertainties related to the roughness coefficients, Manning coefficients, the downstream boundary and most importantly the errors of the chosen software, HEC-RAS, are serious shortcomings of their study.

     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

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.