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.     

基于变分技术的天气雷达速度退模糊算法的改进研究

为有效地识别和提取多普勒天气雷达风场信息,对Gao和Droegemeier提出的基于变分技术的多普勒雷达径向速度数据退模糊方法进行了改进。原方法中将背景风场、方位和径向速度距离梯度信息同时作为约束条件,对Nyquist数进行校正。但是该方法在迭代过程中使用了大量的数值分析和偏微分方程计算,造成径向速度场过度平滑和数据失真。针对这个问题,改进算法在得到径向速度分析场后,结合原始径向速度观测场,通过图像变化检测法,自动识别存在速度模糊的区域及计算需要校正的Nyquist数,对观测场的模糊区域进行校正。利用强对流天气和台风过程的雷达体扫数据验证了改进算法的可行性,并与原始算法及业务应用的WSR-88D算法对比。结果表明:改进算法有效地解决了原始算法中的不足,恢复真实的风场结构和分布特征,改善了退速度模糊的质量,从而得到更为合理的径向速度观测场;并且该算法退模糊效果优于WSR-88D算法,有助于为科研和业务应用服务。     

An Experimental Characterization of Shear Wave Velocity (V<Subscript>s</Subscript>) in Clean and Hydrocarbon-Contaminated Sand

The characteristics of hydrocarbon-contaminated soils have been among major concerns of geotechnical engineers due to its significant frequency of event and also its influential consequences on our surroundings from various environmental and engineering viewpoints. Heretofore, the effects of diverse kinds of hydrocarbon contaminants on majority of geotechnical properties of fine- and coarse-grained soils such as grain size, hydraulic conductivity, plasticity, compressibility, internal friction, cohesion, and shear strength have been investigated. However, there has not been a concentrated research study examining shear wave velocity (\({\text{V}}_{\text{s}}\)) of hydrocarbon-contaminated soils as an important geotechnical property of soil due to this fact that, in small/very small strain levels, the maximum shear modulus of soils (\({\text{G}}_{ \hbox{max} }\)) can be determined using shear wave velocity (\({\text{G}}_{ \hbox{max} } =\uprho{\text{V}}_{\text{s}}^{2}\)). This paper aims to investigate effects of hydrocarbon contamination on shear wave velocity of sandy soils by comparing shear wave velocities in identically prepared clean and contaminated samples. To this aim, an Iranian light crude oil, a standard type of silica sand (Ottawa sand), and a bender element apparatus were used to minutely measure shear wave velocity of clean and crude oil contaminated sand samples. Moreover, dry and quasi-moist tamping methods were employed in order to provide comparable clean and contaminated specimens (containing 4, 6, 8, 10, and 12 wt% of crude oil), respectively. Firstly, a comprehensive bender element (BE) and resonant column tests were conducted on the identically prepared clean sand samples at various amounts of frequency (2–20 kHz) and under various confining pressure (50–500 kPa) to find the best methods of accurately determining shear wave travel time in BE tests. Thereafter, BE tests were conducted to examine shear wave velocity in contaminated specimens. Based on the results, it was found that there was a critical value for crude oil content with the maximum shear wave velocity so that shear wave velocity of 4 wt% contaminated sand (V_s-4 wt%) was about 1.2 times higher than clean one (V_s-clean), and contrastingly adding further crude oil up to 6 wt% made a significant reduction in value of shear wave velocity to some extent that V_s-6 wt% was slightly lower than V_s-clean (V_s-6 wt% = 0.95–0.97V_s-clean). Moreover, adding more contaminant (8–12 wt%) into sand had negligible influences on shear wave velocity. In this paper, the effects of crude oil contamination on sand microstructure were also evaluated using scanning electron microscopy.     

Analyses of Inclined Cracks Neighboring Two Iso-Path Cracks in Rock-Like Specimens Under Compression

Crack propagation process in pre-cracked rock like specimens has been studied experimentally and numerically considering three cracks in the middle part of each specimen. The rock-like specimens are specially prepared from Portland pozzolana cement, fine sands and water. These pre-cracked cylindrical specimens (each containing a single inclined crack in the neighborhood of two iso-path cracks) are experimentally tested under compressive loading. The same problems are numerically simulated by a modified displacement discontinuity method using higher order displacement discontinuity elements and higher order special crack tip elements for crack tip treatment to increase the accuracy of the Mode I and Mode II stress intensity factors obtained based on linear elastic fracture mechanics theory. The crack propagation and coalescence paths of the inclined crack are estimated by implementing a suitable iteration algorithm of incremental crack length extension in a direction predicted by using the maximum tangential stress criterion. The numerical and analytical crack extension analyses are compared which are in good agreement and show the validity, applicability and accuracy of the present work.     

Petrogenesis of the Late Cretaceous Tholeiitic Volcanism and Oceanic Island Arc Affinity of the Chagai Arc, Western Pakistan

The Late Cretaceous Chagai arc outcrops in western Pakistan, southern Afghanistan and eastern Iran. It is in the Tethyan convergence zone, formed by northward subduction of the Arabian oceanic plate beneath the Afghan block. The oldest unit of the Chagai arc is the Late Cretaceous Sinjrani Volcanic Group. This is composed of porphyritic lava flows and volcaniclastic rocks, and subordinate shale, sandstone, limestone and chert. The flows are fractionated low-K tholeiitic basalts, basaltic-andesites, and andesites. Relative enrichment in their LILE and depletion in HFSE, and negative Nb and Ta and positive K, Ba and Sr anomalies point to a subduction-related origin. Compared to MORB, the least fractionated Chagai basalts have low Na2O, Fe2O3T, CaO, Ti, Zr, Y and 87Sr/86Sr. Rather than an Andean setting, these results suggest derivation from a highly depleted mantle in an intraoceanic arc formed by Late Cretaceous convergence in the Ceno-Tethys. The segmented subduction zone formed between Gondwana and a collage of small continental blocks (Iran, Afghan, Karakoram, Lhasa and Burma) was accompanied by a chain of oceanic island arcs and suprasubduction ophiolites including Semail, Zagros, Chagai-Raskoh, Kandahar, Muslim Bagh, Waziristan and Kohistan-Ladakh, Nidar, Nagaland and Manipur. These complexes accreted to the southern margin of Eurasia in the Late Cretaceous.     

Abundance and Ecology of Endemic Asterorotalia trispinosa from the Western Bay of Bengal: Implications for its Application as a Paleomonsoon Proxy

A total of 97 surface sediment samples were collected from the continental shelf and slope of the western Bay of Bengal to assess the potential application of Asterorotalia trispinosa as a paleomonsoon proxy. The relative abundance of living as well as total (living and dead) A. trispinosa ranges from being absent to a maximum of ~31%. The highest abundance of A. trispinosa is close to the outfall region of the Ganga-Brahmaputra Rivers and decreases away from the river mouths. Contrary to that, A. trispinosa is rare in front of both the Cauvery and Pennar river outfall regions, in the central part of the western Bay of Bengal. The living A. trispinosa abundance is the lowest in between two major river systems, viz. Ganga-Brahmaputra-Mahanadi and Krishna-Godavari. The relative abundance of both the total and living A. trispinosa is strongly positively correlated with ambient seawater temperature, and negatively correlated with %Corg and salinity. Based on the spatial distribution, we conclude that A. trispinosa is stenohaline in nature, rather than euryhaline, and further that the increased relative abundance of A. trispinosa indicates warmer and only marginally hyposaline environment. Even though the ecological preference of A. trispinosa suggests it as a potential paleomonsoon proxy, the restricted distribution implies limited application.     

Groundwater arsenic contamination and its health effects in India

During a 28-year field survey in India (1988–2016), groundwater arsenic contamination and its health effects were registered in the states of West Bengal, Jharkhand, Bihar and Uttar Pradesh in the Ganga River flood plain, and the states of Assam and Manipur in the flood plain of Brahamaputra and Imphal rivers. Groundwater of Rajnandgaon village in Chhattisgarh state, which is not in a flood plain, is also arsenic contaminated. More than 170,000 tubewell water samples from the affected states were analyzed and half of the samples had arsenic >10 μg/L (maximum concentration 3,700 μg/L). Chronic exposure to arsenic through drinking water causes various health problems, like dermal, neurological, reproductive and pregnancy effects, cardiovascular effects, diabetes mellitus, diseases of the respiratory and gastrointestinal systems, and cancers, typically involving the skin, lungs, liver, bladder, etc. About 4.5% of the 8,000 children from arsenic-affected villages of affected states were registered with mild to moderate arsenical skin lesions. In the preliminary survey, more than 10,000 patients were registered with different types of arsenic-related signs and symptoms, out of more than 100,000 people screened from affected states. Elevated levels of arsenic were also found in biological samples (urine, hair, nails) of the people living in affected states. The study reveals that the population who had severe arsenical skin lesions may suffer from multiple Bowens/cancers in the long term. Some unusual symptoms, such as burning sensation, skin itching and watering of eyes in the presence of sun light, were also noticed in arsenicosis patients.     

Bearing capacity of sandy soil treated by Kenaf fibre geotextile

Bio-based materials are widely used recently in order to introduce a more sustainable construction material. Kenaf is a type of bio-based material that can be easily obtained in a tropical country, which could be a potential material to be utilised as a geotextile material because it has good tensile strength. The geotextile could be used to improve the bearing capacity of a loose soil. This paper presents a series of small-scale physical modelling tests to investigate the bearing capacity performance of Kenaf fibre geotextile laid on and inside the sand layer. A rigid footing was used to replicate a strip footing during the loading test, and sand was prepared based on 50% of relative density in a rigid testing chamber for ground model preparation. In order to treat the soil, Kenaf fibre geotextile was laid at four difference locations which are on the soil surface and underneath the ground model surface at 50, 75 and 100 mm deep. It was found that the usage of the Kenaf fibre geotextile has improved the bearing capacity of the sandy soil up to 414.9% as compared to untreated soil. It was also found that the depth of the Kenaf fibre geotextile treated into the soil also affects the soil performance.     

Delineation of groundwater potential zones using remote sensing,GIS, and AHP technique in Tehran–Karaj plain,Iran

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.     

Comparing different statistical models for assessing Fe-contaminated soils based on VNIR/SWIR spectral data

Two statistical models including partial least squares regression (PLSR) and principal component regression were comparatively utilized to determine the predictive accuracy of visible–near-infrared and short-wave infrared reflectance spectroscopy in quantifying the Fe concentration in contaminated soils. Two scenarios were applied to select the best model: Scenario I included all wavelengths (400–2450 nm) and Scenario II encompassed characteristic bands of Fe. Pre-processing techniques used to select the best model included: first and second derivatives (FD and SD), multiplicative scatter correction (MSC) and standard normal variate. The abilities of the predictive models were evaluated by splitting soil samples into two random groups (80 and 20%). The first group (80%) was used to evaluate calibration and validation sets by employing the cross‐validation method, and the second group (20%) was applied to test the models. The coefficient of determination (R ²), root mean square error and residual prediction deviation were calculated to evaluate the models. Applying Scenario I indicated that the PLSR model with SD pre-processing was a more accurate technique for predicting the Fe concentration, whereas in the Scenario II, the PLSR model with MSC pre-processing had a better performance. Comparing Scenarios I and II indicated that the more reliable models for predicting the soil Fe content could be constructed by the PLSR model with the SD pre-processing techniques and all wavelengths. The modeling results produced by the PLSR model with the SD pre-processing could be used to detect, map and monitor Fe-contaminated soils by proximal and remote sensing in the mining areas.