Hydrograph separation and development of empirical relationships using single-parameter digital filters

A reliable hydrograph separation method is necessary for surface runoff modeling and hydrological studies. This paper investigates and compares the separation characteristics of two single-parameter digital filters, which are herein referred to as the one-parameter algorithm and the conceptual method. The application of the one-parameter algorithm was found to be restricted to low and medium baseflow separations, with a maximum separation limit of 50% of the total runoff hydrograph. The one-parameter algorithm was also observed to produce unrealistic sharp peaks under the peaks of the measured hydrograph when recession constant is smaller than 0.96. On the other hand, the conceptual method is applicable even for catchments fed largely by groundwater discharge. However, a reliable estimation of recession constant is a prerequisite for applying the conceptual method for large baseflow separations. Based on the hydrograph separation results, useful empirical relationships were developed for a partially urbanized watershed to estimate total runoff and direct runoff from the measured rainfall depth. The relationships between rainfall depth and total runoff depth and rainfall depth and direct runoff depth were found to be well represented by linear equations. The empirical relationships were then applied to estimate the long-term contribution of baseflow and surface runoff to total runoff at the study site. Baseflow was found to contribute about 58–61% of the annual total runoff.

Performances of rainfall-runoff models calibrated over single and continuous storm flow events

Accurate parameter estimation is important for reliable rainfall-runoff modeling. Previous studies emphasize that a sufficient length of continuous events is required for model calibration to overcome the effect of initial conditions. This paper investigates the feasibility of calibrating rainfall-runoff models over a number of limited storm flow events. For a subcatchment having a moderate influence from initial soil moisture conditions, this study shows that rainfall-runoff models could still be calibrated reliably over a set of representative events provided that the events cover a wide range of peak flow, total runoff volume, and initial soil moisture conditions. This approach could provide an alternative calibration strategy for a small watershed that has a limited data length but consists of runoff events with a wide range of magnitudes. Compared to continuous-event calibration, event-based calibration appears to perform better in simulating the overall shape of hydrograph, peak flow and time to peak. However, continuous-event calibration was found to be more reliable in providing runoff volume, suggesting that continuous-event calibration should still be used when runoff volume is the main concern of a study.

Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.

Parameter optimization of kernel-based one-class classifier on imbalance learning

Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate v. In this paper, we proposed a general framework to involve the majority class in solving the parameter selection problem. In this framework, we first use the minority target class for training in the one-class classification stage; then we use both minority and majority class for estimating the generalization performance of the constructed classifier. This generalization performance is set as the optimization criteria. We employed the Grid search and Experiment Design search to attain various parameter settings. Experiments on UCI and Reuters text data show that the parameter optimized one-class classifiers outperform all the standard one-class learning schemes we examined.

Parameter estimation of one-class SVM on imbalance text classification

Compared with conventional two-class learning schemes, one-class classification simply uses a single class for training purposes. Applying one-class classification to the minorities in an imbalanced data has been shown to achieve better performance than the two-class one. In this paper, in order to make the best use of all the available information during the learning procedure, we propose a general framework which first uses the minority class for training in the one-class classification stage; and then uses both minority and majority class for estimating the generalization performance of the constructed classifier. Based upon this generalization performance measurement, parameter search algorithm selects the best parameter settings for this classifier. Experiments on UCI and Reuters text data show that one-class SVM embedded in this framework achieves much better performance than the standard one-class SVM alone and other learning schemes, such as one-class Naive Bayes, one-class nearest neighbour and neural network.

Ensemble parameter estimation for graphical models

Parameter Estimation is one of the key issues involved in the discovery of graphical models from data. Current state of the art methods have demonstrated their abilities in different kind of graphical models. In this paper, we introduce ensemble learning into the process of parameter estimation, and examine ensemble parameter estimation methods for different kind of graphical models under complete data set and incomplete data set. We provide experimental results which show that ensemble method can achieve an improved result over the base parameter estimation method in terms of accuracy. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.

Runoff absorption potential of roofing materials

The main objectives of this work are to establish a relationship between solar radiation and equivalent temperatures for the radiation heat source (oven) to be used in the laboratory and to determine the impact of solar radiation on the absorption and evaporation potential of roofing tiles (glazed and unglazed). Based on the results obtained, it is justifiable to conclude that solar radiation do affect the evaporation and absorption potential of the glazed and unglazed tiles. There is a trend of decrease in both the absorption and evaporation potential of both tiles when exposed to decreasing solar radiation. The evaporation potential of the roof tiles is much higher than its absorption potential. This is clearly displayed in both types of tiles.

Can we obtain realistic parameter estimates for the 'protection for sale' model?

In the Grossman and Helpman (1994) model of endogenous trade protection, sectoral lobbies try to influence an incumbent government that maximizes a weighted sum of political contributions and aggregate welfare. We empirically investigate this model using U.S. and Turkish data. Our specification is more tightly tied to theory than those in existing studies. Additionally, we assume all specific‐factor owners to be organized into different lobbies. These changes, validated by hypothesis tests, yield more realistic parameter estimates of the government's concern for aggregate welfare and of the fraction of population organized into lobbies.

A new modelling approach to simulate preferential flow and transport in water repellent media: parameter sensitivity, and effects on crop growth and solute leaching

A modified version of the popular agrohydrological model SWAP has been used to evaluate modelling of soil water flow and crop growth at field situations in which water repellency causes preferential flow. The parameter sensitivity in such situations has been studied. Three options to model soil water flow within SWAP are described and compared: uniform flow, the classical mobile-immobile concept, and a recent concept accounting for the dynamics of finger development resulting from unstable infiltration. Data collected from a severely water-repellent affected soil located in Australia were used to compare and evaluate the usefulness of the modelling options for the agricultural management of such soils.

The study shows that an assumption of uniform flow in a water-repellent soil profile leads to an underestimation of groundwater recharge and an overestimation of plant transpiration and crop production. The new concept of modelling taking finger dynamics into account provides greater flexibility and can more accurately model the observed effects of preferential flow compared with the classical mobile–immobile concept. The parameter analysis indicates that the most important factor defining the presence and extremity of preferential flow is the critical soil water content.

Comparison of the modelling results with the Australian field data showed that without the use of a preferential flow module, the effects of the clay amendments to the soil were insufficiently reproduced in the dry matter production results. This means that the physical characteristics of the soil alone are not sufficient to explain the measured increase in production on clay amended soils. However, modelling with the module accounting for finger dynamics indicated that the preferential flow in water repellent soils that had not been treated with clay caused water stress for the crops, which would explain the decrease in production.

A parameter adjustment method for relevance feedback

Various relevance feedback techniques have been applied in Content-Based Image Retrieval (CBIR). By using relevance feedback, CBIR allows the user to progressively refine the system's response to a query. In this paper, after analyzing the feature distributions of positive and negative feedbacks, a new parameter adjustment method for iteratively improving the query vector and adjusting the weights is proposed. Experimental results demonstrate the effectiveness of this method.

Intelligent assistant to re-configure parameter-driven systems

Parameter-Driven Systems (PDS) are widely used in commerce for large-scale applications. Reusability is achieved with a PDS design by relocating implicit control structures in the software and the storage of explicit data in database files. This approach can accommodate various user requirements without tedious modification of the software. In order to specify appropriate parameters in a system, knowledge of both business activities and system behaviour are required. For large, complex software packages, this task becomes time consuming and requires specialist knowledge, yet the consistency and correctness still cannot be guaranteed. My research studied the types of knowledge required and agents involved in the PDS customisation. The work also identified the associated problems and constraints. A solution is proposed and implemented as an Intelligent Assistant prototype than a manual approach. Three areas of achievement have been highlighted: 1. The characteristics and problems of maintaining parameter instances in a PDS are defined. It is found that the verification is not complete with the technical/structural knowledge alone, but a context is necessary to provide semantic information and related business activities (thus the implemented parameters) so that mainline functions can relate with each other. 2. A knowledge-based modelling approach has been proposed and demonstrated via a practical implementation. A Specification Language was designed which can model various types of knowledge in a PDS and encapsulate relationships. The Knowledge-Based System (KBS) developed verifies parameters based on the interpreted model of a given context. 3. The performance of the Intelligent Assistant prototype was well received by the domain specialist from the participating organisation. The modelling and KBS approach developed in my research offers considerable promise in solving practical problems in the software industry.

Why do Australians, New Zealanders and Americans travel to Fiji? Evidence from cointegration, unit roots and parameter stability tests

The aim of this study was to estimate the demand for Fiji’s tourism from its three main source markets—Australia, New Zealand, and the US—using the bounds testing approach to cointegration. Our main finding was that visitor arrivals to Fiji and its key determinants are cointegrated over the 1970–2000 period. We then used the autoregressive distributed lag model to estimate short-run and long-run elasticities and found that income in origin countries, transport costs, and prices were significant determinants of Fiji’s tourism demand. We also found that coups negatively impact visitor arrivals from all markets. In testing for parameter stability, we established that the series were integrated of order one in the presence of a structural break. We then used the Hansen test for parameter stability and found that the parameters of our long-run model are stable over time.

Determining RF MEMS switch parameter by neural networks

A challenge in designing a RF MEMS switch is the determination of its parameters to satisfy the application requirements. Often this is done through a set of comprehensive time consuming simulations. This paper employs neural networks and develops a supervised learner that is capable of determining S11 parameter for a RF MEMS shunt switch. The inputs are the length its L and the height of its gap. The outputs are S11s for eight different frequency points from 0 to V band. The developed learner helps prevent repetitive simulations when designing the specified switch. Simulation results are presented.

Parameter sensitivity analysis of surface plasmon resonance biosensor through numerical simulation

Surface based analytical tools have gained more importance for rapid, sensitive and label-free monitoring of molecular recognition events. Surface plasmon resonance (SPR) has played a prominent role in real time monitoring of surface binding events. SPR is increasing its significance especially for the study of ultrathin dielectric layer. This paper investigates the role of thin films of gold, silver and aluminium for protein detection in SPR biosensors. It is shown that the sensitivity, which is indicated by the shift of plasmon dip, is not linearly related to the thickness of protein but quadratic over a specific range. The approach involves a plot of a reflectivity curve as a function of the angle of incidence.