Barry J. Adams

HONEY-BEE MATING OPTIMIZATION (HBMO) ALGORITHM FOR OPTIMAL RESERVOIR OPERATION

In recent years, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Ease of use, broad applicability, and global perspective may be considered as the primary reason for their success. The honey-bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is presented and tested with a nonlinear, continuous constrained problem with continuous decision and state variables to demonstrate the efficiency of the algorithm in handling the single reservoir operation optimization problems. It is shown that the performance of the model is quite comparable with the results of the well-developed traditional linear programming (LP) solvers such as LINGO 8.0. Results obtained are quite promising and compare well with the final results of the other approach.

HYDROLOGIC ANALYSIS OF URBAN CATCHMENTS WITH EVENT-BASED PROBABILISTIC MODELS. 1. RUNOFF VOLUME

The frequency distributions of rainfall volume, rainfall duration, and interevent time are examined using the historical rainfall record of Toronto, Canada. Exponential probability density functions are found to fit well to the histograms of the rainfall event characteristics. The rainfall-runoff transformation on an event basis is described by an equation which incorporates the hydrologic processes commonly considered in numerical simulation models. On the basis of this equation and the exponential probability density functions of rainfall event characteristics, closed-form analytical expressions are derived for average annual runoff volume and runoff event volume return period. Deterministic continuous simulation of various urban catchments are conducted using the Toronto historical rainfall record as input. Close agreement between simulation model results and those from analytical expressions is obtained. The event-based probabilistic models for the determination of average annual runoff volumes and runoff event volumes with specified return periods from urban catchments are proposed as an alternative to continuous simulation models.

Long‐term planning methodology for water distribution system rehabilitation

The most expensive component of a water supply system is the distribution network. Deterioration due to aging and stress causes increased operation and maintenance costs, water losses, reduction in the quality of service, and reduction in the quality of water supplied. In this paper an approach is proposed in which the water distribution network economics and hydraulic capacity are analyzed simultaneously over a predefined analysis period while the deterioration over time of both the structural integrity and the hydraulic capacity of every pipe in the system is explicitly considered. The cost associated with each pipe in the network is calculated as the present value of an infinite stream of costs. In Kleiner et al. [this issue] a methodology is presented to implement this approach into a decision support system that facilitates the identification of an optimal rehabilitation strategy.

An analytical probabilistic approach to sizing flood control detention facilities

Flood control detention facilities have been traditionally designed using the design storm approach. Because of the deficiencies associated with the design storm approach, continuous simulation using long-term historical rainfall data has been recommended for the planning and design of these facilities in order to examine the performance of the system under a wide range of meteorological and hydrological conditions. An analytical probabilistic approach is presented as a computationally efficient alternative to continuous simulation. In this approach the probability distribution of the peak outflow rate from a detention facility servicing an urban catchment is derived from the probability distribution of the rainfall event characteristics that generate the runoff received by the detention facility. These derived mathematical expressions are used to determine analytically the storage-discharge relationship required for a detention facility to achieve the desired level of flood control. Comparison with long-term continuous simulation modeling demonstrates that the closed-form mathematical expressions of the analytical probabilistic approach provide reasonable approximation of the continuous simulation results. The analytical probabilistic approach overcomes some of the conceptual problems of the design storm approach and is therefore proposed for the planning and design of flood control detention facilities.

Hydrologic analysis of urban catchments with event‐based probabilistic models: 2. Peak discharge rate

The peak discharge rate of a runoff event is estimated from the runoff event volume, the duration of the causal rainfall event, and the catchment time of concentration. The runoff event volume is determined from the rainfall-runoff relationship developed by Guo and Adams [this issue]. The average catchment time of concentration is used for all runoff events and is treated as a physical characteristic of the urban catchment, independent of input rainfall characteristics. Incorporating the exponential probability density functions of rainfall event characteristics, closed-form analytical expressions are then derived for the return period of peak discharge rate or flood frequency distribution of urban catchments. The derived flood frequency distributions compared favorably with sample distributions constructed from continuous simulation results for a range of urban catchments. Thus the derived analytical expressions or probabilistic models for peak discharge rates are proposed as an alternative to simulation modeling or regional analyses for the determination of flood frequencies for urban catchments.

Analysis of detention ponds for storm water quality control

Flow capture efficiency and average detention time are the performance measures commonly used in assessing the long-term pollutant removal effectiveness of storm water detention ponds. A statistical formulation is presented for estimating these two performance measures for typical detention ponds where outflow is controlled by an orifice or weir type structure. The flow capture efficiency is determined with the estimation of the total spill volume. The total spill volume is calculated as the combination of the event spill volume and the carryover spill volume. Thus the carryover effect of consecutive runoff events is quantified. A closed-form analytical expression is derived for estimating the average volume-weighted detention time, taking into account the variable inflow and outflow rates and the random spacing between runoff events. Analytical determinations of the average detention time are confirmed by continuous simulation modeling. Statistical solutions of flow capture efficiency closely resemble those obtained from continuous simulation models. The statistical models presented, and the insights gained from their use, can be applied in the design or evaluation of detention ponds for storm water quality control.

Stochastic Optimization of Multi Reservoir Systems Using a Heuristic Algorithm: Case Study From India

A general algorithm developed for stochastic optimization of multireservoir systems is used to determine optimal operational policies for five of the major reservoirs of the Parambikulam-Aliyar irrigation and power project in India. A closed-loop suboptimal policy was determined for the stochastic inflow, deterministic demand problem, subject to storage and cumulative release constraints imposed by an interstate water-sharing agreement. The closed-loop policies were further used to determine optimal rule curves for the major reservoirs. A comparison of the simulated optimal policies and the past performance of the project demonstrates the utility of the derived policies determined with the proposed algorithm.

CROSS CORRELATION ANALYSIS OF RESIDENTIAL DEMAND IN THE CITY OF MILFORD, OHIO

Estimating future demands with a high degree of accuracy in water distribution network design remains an elusive goal. The desired outcome is to match the design demands to the demands that are eventually “realized” in the built system. To this end, this paper explores the cross correlation between demands in an existing system in order to gain a better picture of the representative spatial and temporal patterns of design demands. The aim of the paper is to analyze the cross correlation in the residential demand data collected in the city of Milford, Ohio. More specifically, the paper begins to answer five important questions concerning the cross correlation of the Milford demand data: how strongly cross correlated are indoor, residential demands? How strongly correlated is the deterministic, diurnal component of residential demand? How strongly correlated is the random noise component of residential demand? To what extent does the choice of time step influence the strength of correlation between these 3 demand components? Does the correlation between these 3 demand components differ significantly between weekdays and weekends? To answer these questions, a periodic regression model was used to isolate the deterministic and the random noise components from the residential demand data collected in Milford. Correlation indices were formulated to measure the cross correlation of residential demand, its deterministic, diurnal component, and its random noise component. The Milford results pointed to a number of preliminary findings: (1) both residential demand and its deterministic, diurnal component had a positive and moderate to high correlation, while the random noise component of demand had a low level of correlation for the cases investigated; (2) increasing the time step length (from 600 s to 3,600 s) did increase the strength of the correlation in residential demand and its deterministic, diurnal component. This suggests that a longer time step increases both the coherence in diurnal demand patterns and their synchronicity. It is unclear whether time step length had any influence on the correlation of the random noise component of demand; (3) both residential demand and its deterministic, diurnal component were more strongly correlated during weekend periods than during weekday periods. This finding suggests that weekend periods may be characterized by less erratic water use patterns between customers leading to more coherent and synchronous diurnal patterns. It is unclear whether the random noise component was influenced by day-of-week effects. The implications of these preliminary results are discussed in the context of extended period simulation (EPS) and water quality modeling as they pertain to cost-effective design.

Characterization of Urban Runoff Quality: A Toronto Case Study

This chapter presents an overview of the characterization of urban runoff quality constituents. Characterization includes descriptive statistics, correlation a…

Stochasticity of Demand and Probabilistic Performance of Water Networks

Accurate prediction and modeling of water demand is crucial to understanding the long-term performance of systems, as it is to mounting an effective design and operational planning effort. The paper investigates the influence of cross correlation and autocorrelation in demand on the probabilistic, hydraulic performance of water networks, as measured with the mean and variance of nodal pressures. A stochastic demand model that accounts for lag-1 autocorrelation and lag-0 cross correlation between demands is applied to generate synthetic series of correlated demands. A Monte Carlo Simulation is coupled with EPANET2 to generate time series of pressures and update the mean and variance of nodal pressures. Preliminary results indicate that enforcing a strong lag-0 cross correlation in demand decreases the mean of pressures and increases the variance of pressures. This indicates that the frequency of low-pressure, hydraulic failures is contingent on the level of correlation measured or assumed in a reliability study. Enforcing a strong lag-1 autocorrelation memory at system nodes produces little or no changes in the mean and variance of nodal pressures, but it is found to govern the period of time a pressure signal can persist below a minimum-pressure constraints and remain in a hydraulic failure state.