The reduction of CO
2
emissions is greatly needed in the context of global warming. Among all technologies for reducing CO
2
concentrations, the capture of CO
2
with ionic liquids is a good ...candidate, especially in industry. In this study, a mathematical expression for characterizing the temporal evolution of the CO
2
absorption amount of an ionic liquid is derived using the probability method based on two entropy functions: Shannon entropy and general index entropy. Both the Shannon entropy and general index entropy lead to the same entropic expression, which can model the CO
2
absorption process as the absorption time advances from null to infinity. Its accuracy is validated by comparison with ten experimental datasets with a high average correlation coefficient value of 0.983, a low relative bias value of 0.054 and a low relative root mean square error value of 0.134. Moreover, the proposed maximum CO
2
absorption capacity in the entropic model is presented to be a function of some factors, including the type of ionic liquid, temperature, gas flow rate, and water content. This derived entropic model has a simple mathematical form, showing its potential to predict the temporal variation in the CO
2
absorption amount under some conditions of interest as long as the type of ionic liquid, temperature, gas inflow rate and water content are provided from limited datasets.
Abstract
How the added electrolyte condition and size of primary sediment particles, as well as the particle concentration, affect the rheological behaviours of water–sediment suspension remains to ...be of interest in the sediment field. In this work, rheological experiments of water–kaolinite suspensions with different electrolyte conditions, two particle sizes and 39 solid concentrations were performed. The Bingham fluid model has been adopted to fit the experimental data, and the viscosity and Bingham shear stress values were calculated for each suspension. It has been found that an increase in electrolyte concentration and/or valence leads to a larger viscosity value of the suspension, whereas an increase in electrolyte valence yields a smaller Bingham shear stress value. A simple interpretation based on DLVO theory was presented in this study. It has also been observed that a fine-grained kaolinite suspension corresponds to larger suspension viscosity and Bingham shear stress values. Additionally, some experimental information on the viscosity–solid concentration and Bingham shear stress–solid concentration relationships were also presented in this study. For the viscosity–solid concentration data, the Krieger and Dougherty formula provided the best fit, and a simple exponential relation showed a good fit for the measured shear stress–solid concentration data.
This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, ...Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The objective of this study was to select the maximum number of correlated factors with landslide occurrence for slope-instability mapping and assess landslide susceptibility on Osado Island, Niigata ...Prefecture, Central Japan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN), in a geographic information system (GIS) environment. The landslide inventory data of the National Research Institute for Earth Science and Disaster Prevention (NIED) and a 10-m digital elevation model (DEM) from the Geographical Survey of Institute, Japan, were analyzed. Our study identified fourteen possible landslide-conditioning factors. Considering the spatial autocorrelation and factor redundancy, we applied the CF approach to optimize these set of variables. We hypothesize that if the thematic factor layers of the CF values are positive, it implies that these conditioning factors have a correlation with the landslide occurrence. Therefore, based on this assumption and because of their positive CF values, six conditioning factors including slope angle (0.04), slope aspect (0.02), drainage density network (0.34), distance to the geologic boundaries (0.37), distance to fault (0.35), and lithology (0.31) have been selected as landslide-conditioning factors for further analysis. We partitioned the data into two groups: 70 % (520 landslide locations) for model training and the remaining 30 % (220 landslide locations) for validation. Then, a common ANN model, namely the back-propagation neural network (BPNN), was employed to produce the landslide susceptibility maps. The receiver operating characteristics including the area under the curve (AUC) were used to assess the model accuracy. The validation results indicate that the values of the AUC at optimized and non-optimized BPNN were 0.82 and 0.73, respectively. Hence, it is concluded that the optimized factor model can provide superior accuracy in the prediction of landslide susceptibility in the study area. In this context, we propose a method to select the factors with landslide occurrence. This work is fundamental for further study of the landslide susceptibility evaluation and prediction.
Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine ...learning models, i.e., decision tree (DT) and random forest (RF) approaches to model the massive rainfall-triggered landslide occurrences in the Izu-Oshima Volcanic Island, Japan at a regional scale. At first, a landslide inventory map is prepared consisting of 44 landslide polygons (10,444 pixels) from aerial photo-interpretation and field surveys. To estimate the robustness of the models, we randomly adapted two different samples (S1 and S2), comprising of both positive and negative cells (70% of total landslides - 7293 pixels) for training and remaining (30%–3151 pixels) for validation. Twelve causative factors including altitude, slope angle, slope aspect, plan curvature, total curvature, compound topographic index, stream power index, distance to drainage network, drainage density, distance to geological boundaries, lithology and cumulative rainfall were selected as predictors to implement the landslide susceptibility model. The area under the receiver operating characteristics (ROC) curves (AUC) and other statistical signifiers were used to verify the model accuracies. The result shows that the DT and RF models achieved remarkable predictive performance (AUC > 0.9), producing near accurate susceptibility maps. The overall efficiency of RF (AUC = 0.956) is found significantly higher than the DT (AUC = 0.928) results. Additionally, we noticed that the performance of RF for modeling landslide susceptibility is very robust even though the training and validation samples are altered. Considering the performances, we suggest that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making. Moreover, the RF-based model is promising and enough to be recommended as a method to map regional landslide susceptibility.
Display omitted
•Decision tree and random forest models applied to map landslide-prone areas in a volcanic Island.•Two sample set (S1, and S2) for computing the robustness of the model•LSM maps were compared using different assessment principles.•Random forest performs better on both samples with AUC > 0.9.
In this study, an extended model for describing the temporal evolution of a characteristic floc size of cohesive sediment particles when the flocculation system is subject to a piecewise varied ...turbulent shear rate was derived by the probability methods based on the Shannon entropy theory following Zhu (2018). This model only contained three important parameters: initial and steady-state values of floc size, and a parameter characterizing the maximum capacity for floc size increase (or decay), and it can be adopted to capture well a monotonic pattern in which floc size increases (or decays) with flocculation time. Comparison with 13 literature experimental data sets regarding floc size variation to a varied shear rate showed the validity of the entropic model with a high correlation coefficient and few errors. Furthermore, for the case of tapered shear flocculation, it was found that there was a power decay of the capacity parameter with the shear rate, which is similar to the dependence of the steady-state floc size on the shear rate. The entropic model was further parameterized by introducing these two empirical relations into it, and the finally obtained model was found to be more sensitive to two empirical coefficients that have been incorporated into the capacity parameter than those in the steady-state floc size. The proposed entropic model could have the potential, as an addition to existing flocculation models, to be coupled into present mature hydrodynamic models to model the cohesive sediment transport in estuarine and coastal regions.
The interaction between rock and surrounding water is an important research topic in geotechnical engineering. In this study, the temporal variation in rock moisture content when immersed in water is ...investigated using the probability method based on the entropy theory. An analytical mathematical expression is derived by assuming the rock moisture content to be a random variable, maximizing the entropy function subject to a constraint condition and adopting a hypothesis regarding the rock moisture content cumulative distribution function. The proposed entropy-based expression models the temporal evolution of rock moisture content as immersion time progresses from null to infinity. Furthermore, the derived model is tested against eight experimental data sets from the literature, with good agreement, a high correlation coefficient (0.9689) and a low mean absolute relative error (0.0664). Based on previous experimental results, the impacts of three main factors—rock type, rock porosity and outer water pressure on the maximum capacity for moisture content growth, a key parameter of the proposed expression—are discussed. The entropy-based method is a useful tool for predicting the variation of rock moisture content with different immersion times during the water absorption process.
Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been ...largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.
Abstract
As a key parameter of hydrological process modeling, the near-surface air temperature lapse rate reflects the vertical changes in air temperature characteristics in alpine basins but often ...lacks the support of sufficient ground observation data. This study estimated the lapse rate of the Lhasa River Basin (LRB) from the monthly air temperature dataset (2001–2015), which was derived based on good relationships between the observed air temperature at eight gauged stations and the corresponding gridded land surface temperature of MODIS. The estimated annual average air temperature lapse rate was approximately 0.62 °C/100 m. The monthly lapse rate in different years varied seasonally in the range of 0.45–0.8 °C/100 m; the maximum was in May, and the relatively low value occurred from September to January. The snow cover in the zones with relatively low altitudes showed seasonal variation, which was consistent with the air temperature variation. Permanent snow cover appeared in the area above 5000 m and expanded with increasing elevation.
The flocculation of cohesive fine-grained sediment plays an important role in the transport characteristics of pollutants and nutrients absorbed on the surface of sediment in estuarine and coastal ...waters through the complex processes of sediment transport, deposition, resuspension and consolidation. Many laboratory experiments have been carried out to investigate the influence of different flow shear conditions on the floc size at the steady state of flocculation in the shear flow. Most of these experiments reported that the floc size decreases with increasing shear stresses and used a power law to express this dependence. In this study, we performed a Couette-flow experiment to measure the size of the kaolinite floc through sampling observation and an image analysis system at the steady state of flocculation under six flow shear conditions. The results show that the negative correlation of the floc size on the flow shear occurs only at high shear conditions, whereas at low shear conditions, the floc size increases with increasing turbulent shear stresses regardless of electrolyte conditions. Increasing electrolyte conditions and the initial particle concentration could lead to a larger steady-state floc size.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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