We consider a smart or cognitive user (CU) that operates as a secondary user of a cognitive channel. Before transmission, the CU samples the channel until it estimates that it can be accessed successfully. When the CU transmits a packet, it may nevertheless be unsuccessful because its estimate was wrong. The CU then knows about its failure and stops the ongoing transmission sometime before the transmission ends. Then the CU restarts sensing the channel. In this paper we analyse the total delay experienced by a CU. We derive the first and second moments of the effective transmission time of a packet sent by such a smart user, in view of the fact that the sensing and errors made in transmitting a packet when the channel is actually unavailable, will introduce additional delay. This is similar to a “vacation time” in queues, though it differs from conventional vacation time models because such “sampling vacations” can be beneficial to the future transmission.

Optimal Behaviour of Smart Wireless Users

A survey on cognitive packet networks: Taxonomy, state-of-the-art, recurrent neural networks, and QoS metrics

........................................................................................................................................... i ACKNOWLEDGEMENT ..................................................................................................................... iii TABLE OF CONTENTS ........................................................................................................................iv LIST OF FIGURES ...............................................................................................................................vii LIST OF TABLES............................................................................................................................... viii CHAPTER I ............................................................................................................................................

/pdf/reservoir-management-improvement-under-current-and-future-2h3jlziu65.pdf

Reservoir Management Improvement under Current and Future Climate Conditions Based on Evolutionary Computation Technique Application for Calibrating Hydrological Model and Optimizing Rule Curves

This study, as part of the Australian Rainfall and Runoff revision, investigated and compared the performance of the Monte Carlo (MC) and Design Event (DE) methods to estimate design flood events on ten test catchments located in Australia. The catchments were selected to cover a range of climatic conditions, catchment sizes and characteristics. The methods were tested on their ability to reproduce the design flood quantiles over a range of AEPs from 50% to 1%. The effects of record length on their performance, ability to be applied to ungauged catchments and performance in internal gauges were also investigated. In general, both methods performed well over the range of catchments tested. Tests on the effect of record length showed that the performances can vary significantly based on the period of record used, thus highlighting the need to investigate how representative the available flow data is in the context of any available long-term rainfall records. The tests on the applicability of MC and DE methods in ungauged catchments, generally produced poor model performances, thus suggesting that careful consideration should be given in transposing parameters to an ungauged catchments even when there is data available at a neighbouring gauged catchment. The performance of the models in internal gauges was tested in three internal gauge sites within Mary River catchment. In two of the three sites tested both methods performed reasonably well, despite these sites being relatively high in the catchment. From this testing it was concluded that whilst both the MC and DE approaches generally performed similarly, the advantage of the MC method was in the information on uncertainty provided by the spread of results from individual model runs.

Testing of Monte Carlo and design event models for flood estimation

ABSTRACT Hydrological models are widely used to assess the impact of climate variability and land use change on streamflow. However, it is uncertain whether these models are capable of simulating the impact given parameter interactions and inter-annual climate variability for large dry catchments. Two such catchments with extensive land clearing were selected to test three models – AWBM, Sacramento and SimHyd – in terms of their capacity to simulate streamflow during periods of vastly different levels of forest cover. AWBM outperformed others, and thus was further simplified as AWBM_Ca with minimum performance deterioration to have only one transferable parameter: the average storage capacity, Ca. A reduction of 21–28% in the Ca value for the post-clearing period indicates a decrease in the amount of water stored for transpiration and consequently an increase in runoff-generation capacity. AWBM_Ca can be used to predict the impact of forest cover change on streamflow with different Ca values.

Simple hydrological models for assessing the impact of forest cover change on streamflow for large catchments with a dry tropical climate

Deterministic rainfall-runoff models require parameter calibration with the aim of matching the modeled streamflow record to an observed record as closely as possible. Much attention has been focused on automated methods of model calibration using meta-heuristics, such as genetic algorithms. In this study, two meta-heuristic algorithms were compared: the shuffled complex evolution (SCE) algorithm and a real coded genetic algorithm (GA). The GA was modified with a fitness scaler based on a Boltzmann distribution, which was used to adjust the level of elitism of the selection operator. This was found to improve the performance of the GA. The algorithms were compared using a range of configurations on two test problems - the two parameter Rosenbrock function and the six parameter Hartman function. A relatively small number of objective function evaluations were allowed so as to test the ability of the functions to converge under adverse conditions (that is, limited computational runtime). These functions were found to be best solved with different strategies. The Hartman function was found to respond better to a more elitist strategy, and the Rosenbrock function was solved with a more egalitarian strategy with higher population diversity. On both functions, the best configuration of the SCE algorithm was found to perform favorably compared to the best configurations of the GA. The best performing configurations of these algorithms were applied to the calibration of a rainfall runoff model of a catchment in Tasmania. The model was a two-tap Australian Water Balance Model (AWBM) consisting of eight parameters, with 10 years of data and a six month warm up period. 20 trials were performed on each algorithm, and in each trial the data were randomly split into train and test sets of nine and one year’s data respectively. The more egalitarian strategies (which maintain higher population diversity, as taken from the algorithm configurations found to best suit the Rosenbrock function) were found to be favorable for both the SCE and the GA. When comparing results on the test data, this difference was marginal. The difference in performance was more apparent on the results of the SCE algorithm. A recommendation of this paper is to develop a catalogue of the performance of several algorithms under different configurations on a range of test objective functions. This could be used to assist in the configuration of meta-heuristics on a range of applied problems, such as the calibration of additional rainfall runoff models.

Hydrologic Model Parameter Optimisation

Continuous simulation models have found limited application for design flood estimation despite being conceptually attractive. This study, as part of Australian Rainfall and Runoff (ARR) revision, explored the suitability of continuous simulation models for design flood estimation on natural catchments. The ability of continuous simulation models to reproduce design flood quantiles as well as hydrograph behaviour was investigated in 5 catchments under 4 different calibration scenarios and 3 rainfall-runoff models. The scenarios included calibration to: all available record (Scenario 1), a shorter subset of the record (Scenario 2), the larger events (Scenario 3) and the observed flood frequency curve (Scenario 4). For each scenario, the ability of the model to reproduce the observed flood frequency curve (FFC) and the observed hydrograph were tested. The best reproduction of the hydrograph shape and volume was achieved by Scenario 1 and the best reproduction of the observed FFC was obtained by Scenario 4. In four out of the five catchments it was not possible to obtain good fit to both the hydrograph and the observed FFC, indicating that there is a “trade off” between the matching the general hydrograph shape and the FFC.

Testing of continuous simulation models for flood estimation

The Monte Carlo method of design flood estimation is widely accepted as being a rigorous approach to modelling flood frequency of dams, as it can include representative distributions of storm, catchment, and storage characteristics. These include temporal patterns, antecedent catchment conditions, and starting lake storage level. The Monte Carlo method also allows for analysis of other model variables, such as hydrograph timing. Flood frequency of the outflow peak discharges is not the only variable of interest when modelling design floods dams and reservoirs. Also of importance is the time of the storage to rise from a series of warning levels in order to assist with planning for downstream flowing. A catchment and storage model for the dam has been developed, and adapted to Monte Carlo analysis. The suitability of this approach to determining warning times has been assessed. Tens of thousands of design event simulations of the model were executed, with a number of variables recorded for each simulation including peak inflow, peak outflow, peak storage level, and time to reach a given target level from a range of start trigger levels. An attempt was made to fit a statistical distribution to scenario rise times, with mixed results. The scenario rise times were used as qualitative information to guide further investigations, and could also be used for sensitivity analysis to determine parameters critical to short warning times.

Application of the Monte Carlo method to estimate warning times to critical levels in a high consequence dam

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Hydrologic Model Parameter Optimisation

Testing of continuous simulation models for flood estimation

Application of the Monte Carlo method to estimate warning times to critical levels in a high consequence dam

Testing of Monte Carlo and design event models for flood estimation