Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO,BAT and COA algorithms

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.     

Modelling of piping collapses and gully headcut landforms:Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.     

Model updating and seismic loss assessment for a portfolio of bridges

The majority of bridge infrastructures in Italy were built in the 1960s and ‘70s without any specific seismic provision being made. As a consequence, it is expected that these bridges would be highly vulnerable if subjected to a significant seismic event. Given this background, it is natural that the rapid and accurate assessment of economic losses incurred to the bridge infrastructure as a result of such an event could play a crucial role in emergency management in the immediate aftermath of an earthquake. Focusing on the infrastructure system of highway bridges in the Campania region in Italy, this paper demonstrates how both state-of-the-art methodologies in portfolio loss assessment and the available data can be used to assess the probability distribution of the repair costs incurred due to the 1980 Irpinia earthquake. Formulating a probabilistic loss assessment for a portfolio of bridges as a standard Monte Carlo simulation problem helps to resolve the spatial risk integral efficiently. One of the specific features of this case study is the use of statistical methods for updating models of: (a) ground motion predictions, (b) vulnerability/fragility and (c) exposure/costs, based on the available data. It has been observed that alternative hypotheses concerning the ground motion correlation structure can significantly affect the distribution of direct economic losses. Furthermore, updating the ground motion prediction based on available recordings may significantly reduce the dispersion in the estimate of the direct economic losses.     

Assessment of Empirical Pressure–Volume–Temperature Correlations in Gas Condensate Reservoir Fluids: Case Studies

Natural Resources Research - Measurement and modeling of fluid properties and phase behavior of gas condensate reservoir fluids are challenging tasks. Many researchers proposed various empirical...     

Pb/Pb age determinations on the Newania and Sevattur carbonatites of India: evidence for multi-stage histories

The ages of Indian carbonatites are still controversial. Most of the earlier datings were done by K/Ar methods. We therefore analysed Pb/Pb ratios in carbonatites from carbonatite-alkalic complexes of Newania (NW India, Rajasthan State) and Sevattur (SW India, Tamil Nadu State) to constrain the age and geological history of these rocks. Newania carbonatites are intrusive into Precambrian Untala granite-gneiss and mainly dolomitic in composition (rauhaugite) followed by a later phase of ankerite carbonatite, while thin calcite carbonatite (sövite) dykelets are the youngest in the sequence. The analysed whole-rock samples are characterised by ²⁰⁶Pb/²⁰⁴Pb ratios between 60 and 176 and ²⁰⁷Pb/²⁰⁴Pb ratios between 22 and 40, which are extremely high in comparison to common igneous rocks and even for carbonatite compositions. One sample, New 37, shows the extreme ratios of ²⁰⁶Pb/²⁰⁴Pb = 574 and ²⁰⁷Pb/²⁰⁴Pb = 73. This requires a μ-value of about 2000 for the last 1550 Ma. If the samples are classified according to their petrographic/geochemical characteristics this results in an isochron age of 1551 ± 46 Ma for the ankerite carbonatites (six samples). The dolomites (6 samples) yield an isochron age of 2.27 Ga. Although these results fit quite well into the geological evolution scheme of the area, the extreme long age hiatus between dolomite carbonatite and ferrocarbonatite formation events raises severe problems for their petrologic interpretation.

The Proterozoic Sevattur carbonatite complex (SCC, Tamil Nadu) was emplaced contemporaneously with a large number of carbonatite complexes within the Precambrian gneissic terrane of the Eastern Ghats Mobile Belt. The main mass is composed of dolomite carbonatite (rauhaugite) with a few dikes of calcite carbonatite (sövite) and ankerite carbonatite within it. All eight samples together yield an isochron of 805 ± 10 Ma. This isochron is mainly determined on ankerite carbonatites with μ-values up to 1900 for the last 800 Ma. Taking only ankerite carbonatites into account, the resulting age is 801 ± 11 Ma. The ²⁰⁶Pb/²⁰⁴Pb and ²⁰⁷Pb/²⁰⁴Pb ratios of these samples are similar to the main group of Newania and far beyond the isotopic composition of common igneous rocks.

Our investigations show that in carbonatitic rock systems extremely high lead isotopic ratios can be established due to the crystallization of uranium-rich mineral phases. In both cases the observed high to extremely high initial Pb isotope ratios require the residence of the lead in intermediate high-μ reservoirs either within the upper mantle or the crust prior to the carbonatite formation. A high-temperature event, which completely reset the Rb/Sr and K/Ar isotopic systems of Nevania carbonatites, seems to have no influence on the lead isotopic systematics.     

Petrogenesis of Middle-Eocene granitoids and their Mafic microgranular enclaves in central Urmia-Dokhtar Magmatic Arc (Iran): Evidence for interaction between felsic and mafic magmas

Whole rock major and trace element geochemistry together with zircon U-Pb ages and Sr-Nd isotope compositions for the Middle Eocene intrusive rocks in the Haji Abad region are presented. The granitoid hosts, including granodiorite and diorite, yielded zircon U-Pb ages with a weighted mean value of 40.0 ± 0.7 Ma for the granodiorite phase. Mafic microgranular enclaves(MMEs) are common in these plutons, and have relatively low SiO_2 contents(53.04-57.08 wt.%) and high Mg#(42.6-60.1), probably reflecting a mantle-derived origin. The host rocks are metaluminous(A/CNK = 0.69-1.03), arc-related calc-alkaline, and I-type in composition, possessing higher SiO_2 contents(59.7-66.77 wt.%) and lower Mg#(38.6-52.2); they are considered a product of partial melting of the mafic lower crust. Chondritenormalized REE patterns of the MMEs and granitoid hosts are characterized by LREE enrichment and show slight negative Eu anomalies(Eu/Eu* = 0.60-0.93). The host granodiorite samples yield(⁸⁷Sr/⁸⁶Sr);ratios ranging from 0.70498 to 0.70591,positive eNd(t) values varying from +0.21 to +2.3, and TDM2 ranging from 760 to 909 Ma, which is consistent with that of associated mafic microgranular enclaves(⁸⁷Sr/⁸⁶Sr)i = 0.705111-0.705113, ε_Nd(t)= +2.14 to +2.16, T_DM2 = 697-785 Ma). Petrographic and geochemical characterization together with bulk rock Nd-Sr isotopic data suggest that host rocks and associated enclaves originated by interaction between basaltic lower crust-derived felsic and mantlederived mafic magmas in an active continental margin arc environment.     

Phenolic endocrine disrupting chemicals (EDCs) in Anzali Wetland, Iran: elevated concentrations of 4-nonylphenol, octhylphenol and bisphenol A

We have studied the distribution and value of phenolic endocrine disrupting chemicals (EDCs) in surface sediment samples taken from Anzali Wetland, Iran. These samples were collected from 22 stations during the time span of June-May 2010. In each of the sampling stations, we detected 4-nonylphenol (4-NP), octylphenol (OP), and bisphenol A (BPA) with maximal concentrations of 29, 4.3, and 7 μg g(-1) dry weight (dw), respectively. High levels of alkylphenols (APs) and BPA were also found near urban areas. Furthermore there were no significant differences between those stations in terms of the detected levels. One of the important factors in controlling the fate of these compounds in the aquatic environment appeared to be Total Organic Carbon (TOC). Hierarchical cluster analysis showed differences in the biomarker characteristics of EDCs and TOC between the stations. Our findings indicate that EDCs are ubiquitous in sediments from northeast Wetlands of Iran, contaminating the aquatic habitats in this area.     

Vulnerability evaluation of a coastal aquifer via GALDIT model and comparison with DRASTIC index using quality parameters

In recent years, environmental assessments of groundwater resources have resulted in the development of models that help identify the vulnerable zones. An aquifer is investigated using both GALDIT and DRASTIC indices. The GALDIT model is developed to determine the vulnerability of coastal aquifers in terms of saltwater intrusion whereas the DRASTIC model is generally applicable to all aquifers. Having compared the results of both the GALDIT and DRASTIC models with quality parameters, the salinity model proved to be more appropriate in identifying the vulnerability of coastal aquifers. The results show a Pearson correlation coefficient between TDS and the GALDIT vulnerability map of 0.58 while the corresponding value for the DRASTIC index is 0.48.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR A. Fiori