Simulation of suspended sediment based on gamma test,heuristic, and regression-based techniques

In the present study, four different heuristic techniques viz. multi-layer perceptron (MLP), radial basis function (RBF), self-organizing maps (SOM), and co-active neuro-fuzzy inference system (CANFIS) with hyperbolic tangent and sigmoid transfer functions and two regression-based techniques, i.e., multiple linear regression (MLR) and sediment-rating curve (SRC), were used for suspended sediment modeling. Gamma test (GT), correlation function (CF), M test, and trail–error procedure were applied for estimation of appropriate input variables as well as training data length. The results of the GT and CF suggested the five input variables (Q_t, Q_t?1, Q_t?2, S_t?1, and S_t?2, where Q_t?1 and S_t?1 indicate the discharge and sediment values of one previous day) as the best combination. The optimal training data length (75% of total data) was estimated by M test and trail–error procedure for development of the applied models. The MLP with sigmoid transfer function (M-2) performed better than the all other models. The results of sensitivity analysis indicated that the present-day discharge (Q_t), 1-day lag discharge (Q_t?1) and 1-day lag suspended sediment (S_t?1) are the most influenced parameters in modeling current day suspended sediment (S_t).     

Stochastic observation error and uncertainty in water quality evaluation

When evaluating water quality, the influence of physical weight of the observed index is normally taken into account, but the influence of stochastic observation error (SOE) is not adequately considered. Using Monte Carlo simulation, combined with Shannon entropy, the Principle of Maximum Entropy (POME) and Tsallis entropy, this study investigates the influence of stochastic observation error (SOE) for two cases of the observed index: small observation error and large observation error. Randomness and fuzziness represent two types of uncertainties that are deemed significant and should be considered simultaneously when developing or evaluating water quality models. To that end, three models are employed here: two of the models, named as model I and model II, consider both the fuzziness and randomness, and another model, considers only fuzziness. The results from three representative lakes in China show that for all three models, the influence of stochastic observation error (SOE) on water quality evaluation can be significant irrespective of whether the water quality index has a small observation error or a large observation error. Furthermore, when there is a significant difference in the accuracy of observations, the influence of stochastic observation error (SOE) on water quality evaluation increases. The water quality index whose SOE is minimum determines the results of evaluation.     

Galactic evolution and star counts in the milky way

A model of population synthesis for our Galaxy is used to estimate the constraints imposed by photometric and astrometric star counts. We construct a statistical method to compute the degree of compatibility of the model with the data in the form of a multidimensional functionf(V, B-V, U-B, ). We apply the method to the determination of the star formation history in the Galaxy from Schmidt plates towards the anticentre. Although the presently available data do not allow us to draw firm conclusions, we show that the same kind of data with a slightly better photometric accuracy and with proper motions would give new constraints on the past star formation rate of the Galaxy.     

Long‐term hydrological simulation based on the Soil Conservation Service curve number

Presenting a critical review of daily flow simulation models based on the Soil Conservation Service curve number (SCS‐CN), this paper introduces a more versatile model based on the modified SCS‐CN method, which specializes into seven cases. The proposed model was applied to the Hemavati watershed (area = 600 km²) in India and was found to yield satisfactory results in both calibration and validation. The model conserved monthly and annual runoff volumes satisfactorily. A sensitivity analysis of the model parameters was performed, including the effect of variation in storm duration. Finally, to investigate the model components, all seven variants of the modified version were tested for their suitability. Copyright © 2004 John Wiley & Sons, Ltd.     

Horizontal moisture distribution from TIROS-N high resolution infrared sounder (HIRS)

A direct linear relationship between water vapour content of lower, middle and upper-middle troposphere respectively with the radiances for 8.3, 7.3 and 6.7 μm spectral observations has been attempted. From the atmospheric simulations such a relationship is found to exist with a good degree of correlation and is seen to be quite insensitive to temperature changes. Such an approach is used to interpret the water vapour imageries obtained from TIROS-N sounder through construction of detailed water vapour distribution maps.     

Minimum relative entropy theory for streamflow forecasting with frequency as a random variable

This paper develops a minimum relative entropy theory with frequency as a random variable, called MREF henceforth, for streamflow forecasting. The MREF theory consists of three main components: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. MREF is robust at determining the main periodicity, and provides higher resolution spectral density. The theory is evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflows show the coefficient of determination (r ²) of 0.876, which is slightly higher in the Upper Mississippi (r ² = 0.932) than in the Lower Mississippi (r ² = 0.806). Comparison of different priors shows that the prior with the background spectral density with a peak at 1/12 frequency provides satisfactory accuracy, and can be used to forecast monthly streamflow with limited information. Four different entropy theories are compared, and it is found that the minimum relative entropy theory has an advantage over maximum entropy (ME) for both spectral estimation and streamflow forecasting, if additional information as a prior is given. Besides, MREF is found to be more convenient to estimate parameters with cepstrum analysis than minimum relative entropy with spectral power as random variable (MRES), and less information is needed to assume the prior. In general, the reliability of monthly streamflow forecasting from the highest to the lowest is for MREF, MRES, configuration entropy (CE), Burg entropy (BE), and then autoregressive method (AR), respectively.     

Simulation of wave breaking effects in two-dimensional elliptic harbor wave models

A technique is developed for including the effects of dissipation due to wave breaking in two-dimensional elliptic models based on the mild-slope wave equation. This involves exploration of convergence properties pertaining to iteration due to presence of the nonlinear wave breaking parameter in the governing equations as well as new boundary conditions that include wave-breaking effects. Five wave-breaking formulations are examined in conjunction with the resulting model, which is applied to tests involving a sloping beach, a bar-trough bottom configuration, shore-connected and shore-parallel breakwaters on a sloping beach, and two real-world cases. Model results show that three of the formulations, when used within the context of the modeling scheme presented here, provide excellent results compared to data.     

EXPERIMENTAL STUDY ON BED SHEAR STRESS AND SEDIMENT TRANSPORT IN A MEANDERING LABORATORY CHANNEL

1 INTRODUCTION River erosion is a complex phenomenon. The rate of bank retreat is determined by flow, bed topography, sediment transport, bank properties, and water quality. Prediction of future river planform changes and the knowledge of river erosion and river meandering are required for land use planning in alluvial river valleys and determining locations for bridges and hydraulic structures. The control of riverbank erosion requires prediction of flow and bed features in a meanderin…     

Modeling urban leaf area index with AISA+ hyperspectral data

This study used simple multiple regression to model urban leaf area index (LAI) in Terre Haute, Indiana, USA as a function of AISA+ hyperspectral radiance and its derivative features. Regression R² values ranging from 0.27 to 0.73 were obtained from the various models. Features appearing most frequently in the models included radiance at 0.727, 0.753, 0.848, 0.870, 0.900 and 0.917 μm. The best single predictor of LAI was the absolute difference in radiance between 0.777 and 0.673 μm. The best models performed well at low and intermediate LAI levels, but were less accurate with LAI values between 5.0 and 8.0.     

Trend and persistence of precipitation under climate change scenarios for Kansabati basin,India

With increasing uncertainties associated with climate change, precipitation characteristics pattern are receiving much attention these days. This paper investigated the impact of climate change on precipitation in the Kansabati basin, India. Trend and persistence of projected precipitation based on annual, wet and dry periods were studied using global climate model (GCM) and scenario uncertainty. A downscaling method based on Bayesian neural network was applied to project precipitation generated from six GCMs using two scenarios (A2 and B2). The precipitation values for any of three time periods (dry, wet and annual) do not show significant increasing or decreasing trends during 2001–2050 time period. There is likely an increasing trend in precipitation for annual and wet periods during 2051–2100 based on A2 scenario and a decreasing trend in dry period precipitation based on B2 scenario. Persistence during dry period precipitation among stations varies drastically based on historical data with the highest persistence towards north‐west part of the basin. Copyright © 2009 John Wiley & Sons, Ltd.