Flood occurrence hazard forecasting based on geographical information system

The application of Geographical Information system (GIS) in modeling flood and its prediction in catchments offers considerable potential. Several examples illustrate simple GIS techniques to produce flood hazard indices or its zonation using hydrologic-type models. Existing flood models can also be loosely coupled to a GIS, such as the HMS (Hydrological Modeling System) model. Forethermore, models can be fully integrated into a GIS by embedded coupling, such as the SCS (Soil Conservation Service) model. Installation of flood forecasting systems in watersheds with incomplete hydrometric data may reduce the flood-induced damages. In this study Geographical Information system used to up to date the watershed data and estimation of SCS model parameters which is sensible to considered the real time flood forecasting in Kasilian catchment of Mazandaran province. The main aim of this paper is to investigate the possibility of the linkage between GIS with a comprehensive hydrologic model, especially HMS. The use of GIS could produce a suitable agreement between observed results (extracted rainfall and runoff data of 1992, 1995 and 1996 from the related stations) with the calculated results of the hydrological model. The obtained results from rainfall-runoff process simulations of the model in this research showed that submergibility of the main watershed, Kasillian, does not depend on the outlet discharge rate of each one of its watershed independently. But it is related to how those two outlet hydrographs from main river watershed are combined. The model is capable of showing the flood characteristics temporally and spatially in each cross section of the channel network.     

Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.     

3-D dynamic foundation-soil-foundation interaction on layered soil

In this work the interaction between adjacent rigid, surface foundations resting on a viscoelastic layered soil medium is studied. A 3-D frequency domain BEM formulation in conjunction with infinite space fundamental solutions and the so called `successive stiffness method', initially developed for elastostatics and adapted here for the solution of elastodynamic problems, are used for the simulation of a layered soil medium. As a result, a discretization of the soil-foundation interface and the surrounding free surface as well as the soil layers' interfaces is necessary. However, it is shown in this work that reasonably accurate results can be obtained by using a substantially reduced discretization scheme involving only a small portion of the free surface surrounding the foundation and the corresponding interfaces of the soil layers. The presented numerical results demonstrate the importance of the dynamic foundation-soil-foundation interaction phenomenon which becomes even more pronounced where the supporting soil medium is made up of relatively shallow layers close to its free surface.     

The molecular weight distribution of coal extracts. Coalification is not a condensation polymerization

The pyridine extracts from Bruceton and Illinois No. 6 coals were fractionated using gel permeation chromatography and the molecular weight of each fraction was measured using vapor pressure osmometry. If, as has been suggested, coalification is a simple condensation polymerization from a mixture of monomers with gel formation, then as the molecular weight of the components increase, their concentration should decrease. Exactly the opposite behavior is observed. The number average molecular weights for the Bruceton and Illinois No. 6 extracts respectively are 860 daltons and 1100 daltons while the corresponding weight average molecular weights calculated from the molecular weight distribution are 2300 daltons and 2700 daltons.     

Comparing different statistical models for assessing Fe-contaminated soils based on VNIR/SWIR spectral data

Two statistical models including partial least squares regression (PLSR) and principal component regression were comparatively utilized to determine the predictive accuracy of visible–near-infrared and short-wave infrared reflectance spectroscopy in quantifying the Fe concentration in contaminated soils. Two scenarios were applied to select the best model: Scenario I included all wavelengths (400–2450 nm) and Scenario II encompassed characteristic bands of Fe. Pre-processing techniques used to select the best model included: first and second derivatives (FD and SD), multiplicative scatter correction (MSC) and standard normal variate. The abilities of the predictive models were evaluated by splitting soil samples into two random groups (80 and 20%). The first group (80%) was used to evaluate calibration and validation sets by employing the cross‐validation method, and the second group (20%) was applied to test the models. The coefficient of determination (R ²), root mean square error and residual prediction deviation were calculated to evaluate the models. Applying Scenario I indicated that the PLSR model with SD pre-processing was a more accurate technique for predicting the Fe concentration, whereas in the Scenario II, the PLSR model with MSC pre-processing had a better performance. Comparing Scenarios I and II indicated that the more reliable models for predicting the soil Fe content could be constructed by the PLSR model with the SD pre-processing techniques and all wavelengths. The modeling results produced by the PLSR model with the SD pre-processing could be used to detect, map and monitor Fe-contaminated soils by proximal and remote sensing in the mining areas.