Does disaster shocks affect farmers’ willingness for insurance? Mediating effect of risk perception and survey data from risk-prone areas in East China

Natural Hazards - The study estimates the impact of disaster shocks and risk perception on farmers’ willingness for insurance. Based on data of 328 farmers from the Shandong province of East...     

Determination of unsaturated hydraulic conductivity of sandy soils: a new pore network approach

Acta Geotechnica - Hydraulic conductivity is one of the most important characteristics of unsaturated soils. Its determination is essential for modeling various phenomena of interest such as...     

Mass Discharge in a Tracer Plume: Evaluation of the Theissen Polygon Method

A tracer plume was created within a thin aquifer by injection for 299 d of two adjacent “sub‐plumes” to represent one type of plume heterogeneity encountered in practice. The plume was monitored by snapshot sampling of transects of fully screened wells. The mass injection rate and total mass injected were known. Using all wells in each transect (0.77 m well spacing, 1.4 points/m² sampling density), the Theissen Polygon Method (TPM) yielded apparently accurate mass discharge (M_d) estimates at three transects for 12 snapshots. When applied to hypothetical sparser transects using subsets of the wells with average spacing and sampling density from 1.55 to 5.39 m and 0.70 to 0.20 points/m², respectively, the TPM accuracy depended on well spacing and location of the wells in the hypothesized transect with respect to the sub‐plumes. Potential error was relatively low when the well spacing was less than the widths of the sub‐plumes (>0.35 points/m²). Potential error increased for well spacing similar to or greater than the sub‐plume widths, or when less than 1% of the plume area was sampled. For low density sampling of laterally heterogeneous plumes, small changes in groundwater flow direction can lead to wide fluctuations in M_d estimates by the TPM. However, sampling conducted when flow is known or likely to be in a preferred direction can potentially allow more useful comparisons of M_d over multiyear time frames, such as required for performance evaluation of natural attenuation or engineered remediation systems.     

Collapse capacity of soil‐structure systems under pulse‐like earthquakes

In this paper, a comprehensive study is carried out to examine the possibility of dynamic instability produced in soil‐structure systems using an ensemble of 50 pulse‐like records. A number of structural models with various vibration periods varying from 0.1 to 2 s are used in this study. The superstructure is simulated as a non‐linear SDOF oscillator with a two‐segment backbone curve having negative post‐yield stiffness. The soil is idealized based on the cone model concept widely used for practical purposes. The results of this investigation demonstrate that as the pulse period increases, the collapse relative lateral strength ratio decreases and probability of dynamic instability enhances. Moreover, soil flexibility makes the system dynamically more unstable, and as the non‐dimensional frequency increases, the collapse relative lateral strength ratio highly reduces. Additionally, the aspect ratio has insignificant effects on the collapse relative lateral strength ratio. Furthermore, comparison of the collapse relative lateral strength ratios resulting from pulse‐like motions with those obtained from studies under non‐pulse‐like motions (Miranda and Akkar; FEMA 440) for fixed‐base conditions shows that high‐velocity pulses exacerbate the dynamic instability problem and decrease the collapse relative lateral strength ratio. Copyright © 2014 John Wiley & Sons, Ltd.     

On board short-time high temperature heat treatment of ballast water: a field trial under operational conditions

A ballast water short-time high temperature heat treatment technique was applied on board a car-carrier during a voyage from Egypt to Belgium. Ballast water from three tanks was subjected for a few seconds to temperatures ranging from 55 degrees C to 80 degrees C. The water was heated using the vessel's heat exchanger steam and a second heat exchanger was used to pre-heat and cool down the water. The treatment was effective at causing mortality of bacteria, phytoplankton and zooplankton. The International Maritime Organization (IMO) standard was not agreed before this study was carried out, but comparing our results gives a broad indication that the IMO standard would have been met in some of the tests for the zooplankton, in all the tests for the phytoplankton; and probably on most occasions for the bacteria. Passing the water through the pump increased the kill rate but increasing the temperature above 55 degrees C did not improve the heat treatment's efficacy.     

A new holographic dark energy model in Brans-Dicke theory with logarithmic scalar field

We study a holographic dark energy model in the framework of Brans-Dicke (BD) theory with taking into account the interaction between dark matter and holographic dark energy. We use the recent observational data sets, namely SN Ia compressed Joint Light-Analysis (cJLA) compilation, Baryon Acoustic Oscillations (BAO) from BOSS DR12 and the Cosmic Microwave Background (CMB) of Planck 2015. After calculating the evolution of the equation of state as well as the deceleration parameters, we find that with a logarithmic form for the BD scalar field the phantom crossing can be achieved in the late time of cosmic evolution. Unlike the conventional theory of holographic dark energy in standard cosmology (\(\omega_{D}=0\)), our model results in a late time accelerated expansion. It is also shown that the cosmic coincidence problem may be resolved in the proposed model. We execute the statefinder and Om diagnostic tools and demonstrate that interaction term does not play a significant role. Based on the observational data sets used in this paper it seems that the best value with \(1\sigma \) and \(2\sigma \) confidence interval are \(\varOmega_{m}=0.268^{+0.008~+0.010}_{-0.007~-0.009}\), \(\alpha =3.361^{+0.332~+0.483} _{-0.401~-0.522}\), \(\beta =5.560^{+0.541~+0.780}_{-0.510~-0.729}\), \(c=0.777^{+0.023~+0.029}_{-0.017~-0.023}\) and \(b^{2} =0.045\), according to which we find that the proposed model in the presence of interaction is compatible with the recent observational data.     

Prediction of significant wave height using regressive support vector machines

Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H_s). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H_s. Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.     

Machine learning for accelerating 2D flood models: Potential and challenges

Two-dimensional hydrodynamic models numerically solve full Shallow Water Equations (SWEs). Despite their high accuracy, these models have long simulation run times and therefore are of limited use for exploratory or real-time flood predictions. We investigated the possibility of improving flood modelling speed using Machine Learning (ML). We propose a new method that replaces the computationally expensive parts of the hydrodynamic models with simple and efficient data-driven approximations. Our hypothesis is that by integrating ML with physics-based numerical methods, we can achieve improved generalization performance: that is, the trained model for one case study can be used in other studies without the need for new training. We tested two ML approaches: for the first, we integrated curve fitting, and, for the second, artificial neural networks (ANN) with a finite volume scheme to solve the local inertial approximation of the SWEs. The data-driven models approximated the Momentum Equation, which explicitly solved the time derivative of flow rates. Water depths were then updated by applying a water balance equation. We also tested two different training datasets: the simulated dataset, generated from the results of hydrodynamic model, and the random dataset, generated by directly solving the momentum equation on randomly sampled input data. Various combinations of input features, for example, water slope and depth, were explored. The proposed models were trained in a small hypothetical case and tested in a different hypothetical and in two real case studies. Results showed that the curve-fitting method can be implemented successfully, given sufficient training and input data. The ANN model trained with a random dataset was substantially more accurate than that of the model trained with the simulated dataset. However, it was not successful in the real case studies. The curve-fitting method resulted in better generalization performance and increased the simulation speed of the local inertial model by 23%. Future research should test the performance of ML in terms of an increase in stable time step size and approximation of the full SWEs.     

Evaluation of Carcinogenic Risk Due to Accidental Ingestion of PAHs in Contaminated Soils

Polycyclic aromatic hydrocarbons (PAHs) are types of hazardous contaminants, which their ingestion could cause severe consequences on human health. Leakages from storage tanks, underground pipelines, and evaporation ponds are the main sources of soil and groundwater contaminations at the Tehran Oil Refinery area (TOR site), located in south of Tehran, Iran. In this study, soil samples were collected from different locations at and adjacent to a polluted stream in the south of the refinery. The samples were analyzed for two hazardous PAH compounds, namely benzo[a]anthracene and acenaphthene. The clean up levels due to the accidental ingestion of contaminated soils at the site were also investigated in accordance to the U.S.EPA guidelines. Comparing the soil analysis results indicated that the benzo[a]anthracene concentrations in the samples varied from 53 to 299 mg/kg, which were higher than the clean up level of 1.17 mg/kg. Thus, soil remediation is required for this contaminant. The acenaphthene analysis results denoted that the average concentration of this contaminant was below the clean up level of 116.67 mg/kg, indicating that no treatment for this contaminant is necessary at the TOR site. Also, because the slope of the ground extends to the south of the stream, which stimulates the migration of the contaminants in this direction due to advection and dispersion mechanisms, the average of benzo[a]anthracene concentrations in south samples was higher than north samples (i.e., C_avg(S) = 160 ppm, C_avg(N) = 113 ppm). Various treatment techniques such as thermal desorption, soil vapor extraction (SVE), and solidification/stabilization (S/S) were investigated for this site. Due to moderate to high plasticity and relatively low permeability of the soil and low volatility of benzo[a]anthracene, however S/S method is recommended as a practical approach for the remediation of the soil at the site.