Freezing as a method of sample preservation for the analysis of dissolved inorganic nutrients in seawater

It is often desirable or necessary to store collected seawater samples prior to analysis for dissolved inorganic nutrients. It is therefore important to establish preservation and storage techniques that will ensure sample integrity and will not alter the precision or accuracy of analysis. We have performed a series of experiments on the storage of nutrient samples collected at the oligotrophic North Pacific benchmark Station ALOHA, using both standard autoanalyses and low-level techniques. Our results reveal that for oligotrophic oceanic waters, the immediate freezing of an unfiltered water sample in a clean polyethylene bottle is a suitable preservation method. This procedure is simple, it avoids potentially contaminating sample manipulations and chemical additions, and it adequately preserves the concentrations of nitrate + nitrite, soluble reactive phosphate, and soluble reactive silicate within a single water sample.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

Paleoenvironments of the evolving Pliocene to early Pleistocene foreland basin in northwestern Taiwan: An example from the Dahan River section

The overriding of the Luzon volcanic arc atop the underlying Chinese rifted‐continental margin has caused the formation of the Taiwan mountain belts and a peripheral foreland basin west of the orogen since the late Miocene. In this study, lithofacies analysis and calcareous nannofossil biostratigraphic investigations of the Dahan River section in northwestern (NW) Taiwan were performed. Our results offer insights into the temporal evolution of the sedimentary environments and the competing effects of the sedimentation and basin tectonics of the NW Taiwan foreland basin from the Pliocene to early Pleistocene. Nannofossil biostratigraphic studies showed that the upper Kueichulin Formation and the overlying Chinshui Shale can be assigned to the NN15 biozone of the Pliocene age, and the Cholan Formation pertains to NN16–NN18 of the early Pleistocene. The NN15–NN16 boundary coincides roughly with the boundary of the Chinshui Shale and Cholan Formation. We recognized three major sedimentary environments in the studied foreland succession comprising the upper Kueichulin Formation, Chinshui Shale, Cholan Formation and Yangmei Formation, in ascending order. During the deposition of the upper Kueichulin Formation in the early Pliocene, the dominant environment was a wave‐ and tide‐influenced open marine setting. During the late Pliocene, the environment deepened to an outer‐offshore setting when the sediments of Chinshui Shale were accumulated. In the Pleistocene, the environment then shallowed to wave‐dominated estuaries during the deposition of the lower Cholan Formation, and the basin was rapidly filled, generating a meandering and sandy braided river environment during the deposition of the upper Cholan to the Yangmei Formation. In summary, the evolution of sedimentary environments in the studied succession shows a deepening then a shallowing and coarsening upward trend during the period from the Pliocene to the Pleistocene, spanning the age from approximately 4 to 1 Ma.     

Deposition Process of Dust Microparticles from Aerosol to Snow-Firn Pack on Glacier No. 1 in Eastern Tianshan Mountains, China

Samples were continuously collected from aerosol, fresh snow, and snow pits on Glacier No. 1 at Urumqi River source in eastern Tianshan (天山) Mountains. The deposition processes and the characteristics of mineral dust microparticles from aerosol to fresh snow, and then evolution to the snow pit were determined. Total dust microparticle concentration in the surface snow and aerosol showed a similar temporal variation trend, which was strongly associated with regional and local at-mospheric circulation in the ...     

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.     

Applicability of CPT-based methods in predicting toe bearing capacities of driven piles in sand

This paper presents a study on applicability of predicting toe bearing capacities from cone penetration test (CPT) for PHC (pretensioned spun high-strength concrete) driven piles into deep sandy deposits in the Nakdong River deltaic area west of Busan City in South Korea. Using toe bearing capacities obtained from pile driving analyzer (PDA) tests as reference values, which were reliably calibrated by on-site O-cell tests, the applicability of the CPT-based methods was evaluated using a statistical rank index (RI). A total of 82 piezocone penetration test soundings and 190 PDA test piles were used for reliability analysis in this study. Three correction steps were applied to obtain reliable PDA and CPT data sets before ranking is carried out. The RI index is combined from four criteria: (1) the best-fit line, (2) the arithmetic mean and standard deviation, (3) the cumulative probabilities, and (4) the log-normal and histogram distributions. Based on these criteria the performance of some SPT-based methods in the literature is evaluated.     

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.