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Geoscience Frontiers
Scopus
ISSN 1674-9871 · Elsevier BV · 16 artikel di indeks
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Artikel Jurnal Teks lengkap
Adoption of green finance and green innovation for achieving circularity: An exploratory review and future directions
There is growing attention from governments and regulators towards crucial matters such as climate change and global warming, resulting in a pressing need to investigate the factors that make it possible for businesses to engage in green finance (GF). The externality of environmental pollution…
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Fluoride contamination in groundwater: A global review of the status, processes, challenges, and remedial measures
Groundwater pollution by fluoride is one of the prime concerns of the world population due to its toxicity, which results in adverse health impacts. In this paper, we review the current scenario of the fluoride contamination of groundwater in various countries across the globe and its impact on…
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Natural resource scarcity, fossil fuel energy consumption, and total greenhouse gas emissions in top emitting countries
Natural resource scarcity, fossil fuel energy consumption, and total greenhouse gas emissions are critical issues that require immediate attention. With the global population steadily increasing and economies expanding, the demand for natural resources, particularly fossil fuels, has experienced an…
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Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model
Floods are natural hazards that lead to devastating financial losses and large displacements of people. Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area. The design of flood susceptibility maps has been enhanced through use of hybrid…
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Centrifuge modelling of landslides and landslide hazard mitigation: A review
Landslides are serious geohazards that occur under a variety of climatic conditions and can cause many casualties and significant economic losses. Centrifuge modelling, as a representative type of physical modelling, provides a realistic simulation of the stress level in a small-scale model and has…
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Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization
The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models. For this, a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points, which was randomly divided into two…
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Sources, characteristics, toxicity, and control of ultrafine particles: An overview
Air pollution by particulate matter (PM) is one of the main threats to human health, particularly in large cities where pollution levels are continually exceeded. According to their source of emission, geography, and local meteorology, the pollutant particles vary in size and composition. These…
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Maximum depositional age estimation revisited
In a recent review published in this journal, Coutts et al. (2019) compared nine different ways to estimate the maximum depositional age (MDA) of siliclastic rocks by means of detrital geochronology. Their results show that among these methods three are positively and six negatively biased. This…
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Landslide identification using machine learning
Landslide identification is critical for risk assessment and mitigation. This paper proposes a novel machine-learning and deep-learning method to identify natural-terrain landslides using integrated geodatabases. First, landslide-related data are compiled, including topographic data, geological…
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Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques
A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray…
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Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
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…
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Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear…
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Arsenic contamination of groundwater: A global synopsis with focus on the Indian Peninsula
More than 2.5 billion people on the globe rely on groundwater for drinking and providing high-quality drinking water has become one of the major challenges of human society. Although groundwater is considered as safe, high concentrations of heavy metals like arsenic (As) can pose potential human…
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Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensemble learning methods for capturing the relationships between…
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Flood susceptibility modelling using advanced ensemble machine learning models
Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of…
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GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms
Hazards and disasters have always negative impacts on the way of life. Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout the world. The present study aimed to assess and compare the prediction efficiency of…