A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Sustainable groundwater development in the coastal Tra Vinh province in Vietnam under saltwater intrusion and climate change

Three-dimensional transient groundwater flow and saltwater transport models were constructed to assess the impacts of groundwater abstraction and climate change on the coastal aquifer of Tra Vinh province (Vietnam). The groundwater flow model was calibrated with groundwater levels (2007–2016) measured in 13 observation wells. The saltwater transport model was compared with the spatial distribution of total dissolved solids. Model performance was evaluated by comparing observed and simulated groundwater levels. The projected rainfalls from two climate models (MIROC5 and CRISO Mk3.6) were subsequently used to simulate possible effects of climate changes. The simulation revealed that groundwater is currently depleted due to overabstraction. Towards the future, groundwater storage will continue to be depleted with the current abstraction regime, further worsening in the north due to saltwater intrusion from inland trapped saltwater and on the coast due to seawater intrusion. Notwithstanding, the impact from climate change may be limited, with the computed groundwater recharge from the two climate models revealing no significant change from 2017 to 2066. Three feasible mitigation scenarios were analyzed: (1) reduced groundwater abstraction by 25, 35 and 50%, (2) increased groundwater recharge by 1.5 and 2 times in the sand dunes through managed aquifer recharge (reduced abstraction will stop groundwater-level decline, while increased recharge will restore depleted storage), and (3) combining 50% abstraction reduction and 1.5 times recharge increase in sand dune areas. The results show that combined interventions of reducing abstraction and increasing recharge are necessary for sustainable groundwater resources development in Tra Vinh province.

     

A new methodology for computing ionic profiles and disjoining pressure in swelling porous media

A new two-scale computational model is proposed to construct the constitutive law of the swelling pressure which appears in the modified form of the macroscopic effective stress principle for expansive clays saturated by an aqueous electrolyte solution containing multivalent ionic species. The microscopic non-local nanoscale model is constructed based on a coupled Poisson-Fredholm integral equation arising from the thermodynamics of inhomogeneous fluids in nanopores (Density Functional Theory), which governs the local electric double layer potential profile coupled with the ion-particle correlation function in an electrolytic solution of finite size ions. The local problem is discretized by invoking the eigenvalue expansion of the convolution kernel in conjunction with the Galerkin method for the Gauss-Poisson equation. The discretization of the Fredholm equation is accomplished by a collocation scheme employing eigenfunction basis. Numerical simulations of the local ionic profiles in rectangular cell geometries are obtained showing considerable discrepancies with those computed with Poisson-Boltzmann based models for point charges, particularly for divalent ions in calcium montmorillonite. The constitutive law for the disjoining pressure is reconstructed numerically by invoking the contact theorem within a post-processing approach. The resultant computational model is capable of capturing ranges of particle attraction characterized by negative values of the disjoining pressure overlooked by the classical electric double layer theory. Such results provide further insight in the role the swelling pressure plays in the modified macroscopic effective stress principle for expansive porous media.     

Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio,certainty factor,and index of entropy

The main objective of the study was to evaluate and compare the overall performance of three methods, frequency ratio (FR), certainty factor (CF) and index of entropy (IOE), for rainfall-induced landslide susceptibility mapping at the Chongren area (China) using geographic information system and remote sensing. First, a landslide inventory map for the study area was constructed from field surveys and interpretations of aerial photographs. Second, 15 landslide-related factors such as elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to faults, distance to rivers, distance to roads, landuse, NDVI, lithology and rainfall were prepared for the landslide susceptibility modelling. Using these data, three landslide susceptibility models were constructed using FR, CF and IOE. Finally, these models were validated and compared using known landslide locations and the receiver operating characteristics curve. The result shows that all the models perform well on both the training and validation data. The area under the curve showed that the goodness-of-fit with the training data is 79.12, 80.34 and 80.42% for FR, CF and IOE whereas the prediction power is 80.14, 81.58 and 81.73%, for FR, CF and IOE, respectively. The result of this study may be useful for local government management and land use planning.     

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications, including land use planning and developing mitigation strategies and early warning systems. Th...     

An empirical method for approximating canopy throughfall

Rainfall replenishes surface and subsurface water but is partially intercepted by a canopy. However, it is challenging to quantify the rainfall passing through the canopy (i.e. throughfall). This study derives simple‐to‐use empirical equations relating throughfall to canopy and rainfall characteristics. Monthly throughfall is calculated by applying a mass balance model on weather data from Singapore; Vancouver, Canada; and Stanford, USA. Regression analysis is then performed on the calculated throughfall with three dependent variables (i.e. maximum canopy storage, average rainfall depth and time interval between two consecutive rainfall in a month) to derive the empirical equations. One local equation is derived for each location using data from that particular location, and one global equation is derived using data from all three locations. The equations are further verified with calculated monthly throughfall from other weather data and actual throughfall field measurements, giving an accuracy of about 80–90%. The global equation is relatively less accurate but is applicable worldwide. Overall, this study provides a global equation through which one can quickly estimate throughfall with only information on the three variables. When additional weather data are available, one can follow the proposed methodology to derive their own equations for better estimates. Copyright © 2012 John Wiley & Sons, Ltd.     

The Role of Naturally Occurring River Biofilms on the Degradation Kinetics of Diazinon

River biofilms are dominant riverine biota with diverse microorganisms. They have been found to contribute greatly to river self‐purification for removal of nutrients and organic matter. This study intended to investigate the ability of naturally occurring river biofilms with changing seasons for the removal of the organophosphorus pesticide diazinon. Natural river biofilms from spring showed higher ability to remove diazinon (99.9% removal) than those from winter (77%) with light exposure. In contrast to control sets without biofilms under irradiation, 27% of diazinon removal in spring and 22% in winter may result from microbial activity within biofilms. Removal of diazinon by river biofilms could be attributed mostly to degradation due to low sorption capacity of biofilms. Spring biofilms had higher dissipation rates (0.265 and 0.486 d^?1 for biofilms with different growth periods) than winter ones (0.099 and 0.119 d^?1) according to first order model. Higher ability of diazinon removal by spring biofilms may be explained by their higher bacterial and algal biomass comparing to winter biofilms. Naturally occurring river biofilms played a significant role in degradation of diazinon, particularly for those grown in spring. Their potential for use in the treatment of diazinon‐contaminated water has been demonstrated.     

Experimental evidence of the double-porosity effects in geomaterials

Double-porosity is an important characteristic of microstructure in a large range of geomaterials. It designs porous media with connected fissures/fractures or aggregated soils. The origin of double-porosity can be natural or/and it can result from mechanical, chemical or biological damage. The presence of double-porosity can significantly affect the behaviour of geomaterials. In this paper we provide an experimental evidence of the double-porosity effects by performing laboratory experiments. Series of tracer dispersion experiments (in saturated and unsaturated steady-state water flow conditions) in a physical model of double-porosity geomaterial were carried out. For the comparative purposes, experiments of the same type were also performed in a singleporosity model medium. The results clearly showed that the double-porosity microstructure leads to the non-Fickian behaviour of the tracer (early breakthrough and long tail) in both saturated and unsaturated cases.     

Using quartz grain size and shape analysis to distinguish between aeolian and fluvial deposits in the Dallol Bosso of Niger (West Africa)

Size and Fourier-shape characteristics of quartz sand grains were determined by computerized image analysis in order to distinguish between aeolian and fluvial soil parent materials in the Dallol Bosso in Niger. Factor analysis of grain-size distributions gave four sand end-members that can be related to fluvial transport dynamics operating when the sediments were initially deposited. The medium to fine (and more angular shaped) sand fractions are being reworked by wind. Aeolian deposits were well sorted whereas fluvial deposits were poorly sorted in both size and shape. Although gross-shape characteristics (lower harmonics of Fourier series expansion) indicated a common source rock for all sands, the aeolian sands were well rounded whereas the fluvial sands tended to be more angular (upper harmonics of Fourier series).