During the last two decades, the water resources planning and management profession has seen a dramatic increase in the development and application of various types of evolutionary algorithms (EAs). This observation is especially true for application of genetic algorithms, arguably the most popular of the several types of EAs. Generally speaking, EAs repeatedly prove to be flexible and powerful tools in solving an array of complex water resources problems. This paper provides a comprehensive review of state-of-the-art methods and their applications in the field of water resources planning and management. A primary goal in this ASCE Task Committee effort is to identify in an organized fashion some of the seminal contributions of EAs in the areas of water distribution systems, urban drainage and sewer systems, water supply and wastewater treatment, hydrologic and fluvial modeling, groundwater systems, and parameter identification. The paper also identifies major challenges and opportunities for the future, ...

State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management

[1] Prewhitening has been used to eliminate the influence of serial correlation on the Mann-Kendall (MK) test in trend-detection studies of hydrological time series. However, its ability to accomplish such a task has not been well documented. This study investigates this issue by Monte Carlo simulation. Simulated time series consist of a linear trend and a lag 1 autoregressive (AR(1)) process with a noise. Simulation results demonstrate that when trend exists in a time series, the effect of positive/negative serial correlation on the MK test is dependent upon sample size, magnitude of serial correlation, and magnitude of trend. When sample size and magnitude of trend are large enough, serial correlation no longer significantly affects the MK test statistics. Removal of positive AR(1) from time series by prewhitening will remove a portion of trend and hence reduces the possibility of rejecting the null hypothesis while it might be false. Contrarily, removal of negative AR(1) by prewhitening will inflate trend and leads to an increase in the possibility of rejecting the null hypothesis while it might be true. Therefore, prewhitening is not suitable for eliminating the effect of serial correlation on the MK test when trend exists within a time series.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2001WR000861

Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test

Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review

The differences in spatial patterns of drought over a range of time scales were analysed by the Standardized Precipitation Index (SPI). In a climatic area with a wide range of precipitation characteristics (the Iberian Peninsula), Pearson III distribution is flexible enough to calculate the drought index on different time scales. The Pearson III distribution was adapted to precipitation frequencies at time scales of 1, 3, 6, 12, 24 and 36 months. Spatial patterns of drought were analysed by Principal Component Analysis. The number of components found increased as the time scale did, which indicates great spatial complexity in drought analysis and uncertainty in drought classification, mainly at scales of 24 or 36 months, since the relationships between SPI series of observatories becomes more distant as the time scale increases. We concluded that there were no homogeneous regions with similar drought patterns that could be used for effective drought management or early warning.

Differences in Spatial Patterns of Drought on Different Time Scales: An Analysis of the Iberian Peninsula

Uncertainty and climate change impact on the flood regime of small UK catchments

[1] A new multiobjective formulation is proposed for the optimal design and rehabilitation of a water distribution network, with minimization of life cycle cost and maximization of performance as objectives. The life cycle cost is considered to comprise the initial cost of pipes, the cost of replacing old pipes with new ones, the cost of cleaning and lining existing pipes, the expected repair cost for pipe breaks, and the salvage value of the pipes that are replaced. The performance measure proposed in this study is a modification to the resilience index to suit application to water distribution networks with multiple sources. A new heuristic method is proposed to obtain the solution for the design and rehabilitation problem. This heuristic method involves selection of various design and rehabilitation alternatives in an iterative manner on the basis of the improvement in the network performance as compared to the change in the life cycle cost on implementation of the alternatives. The solutions obtained from the heuristic method are used as part of the initial population set of the multiobjective, nondominated sorting genetic algorithm (NSGA-II) in order to improve the search process. Using a sample water distribution network, the modified resilience index proposed is shown to be a good indicator of the uncertainty handling ability of the network.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006WR005316

Performance‐based optimal design and rehabilitation of water distribution networks using life cycle costing

Investigation and comparison of sampling properties of L-moments and conventional moments

A multiobjective optimization framework for optimal waste load allocation in rivers is proposed, considering (1) the total treatment cost, (2) the equity among the waste dischargers, and (3) a comprehensive performance measure that reflects the dissolved oxygen (DO) violation characteristics. This framework consists of an embedded river water quality simulator that has a gradually varied flow module and a pollutant transport module, which simulates the transport process including reaction kinetics (in terms of biochemical oxygen demand-DO). The outer shell of the framework consists of the two nonseasonal, deterministic, multiobjective waste load allocation planning models, namely, cost-performance model and cost-equity-performance model. These models are solved using a powerful and recently developed multiobjective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II. The practical utility of the multiobjective framework in decision-making is illustrated through a realistic example of the Willamette River in the state of Oregon.

Multiobjective Optimal Waste Load Allocation Models for Rivers Using Nondominated Sorting Genetic Algorithm-II

http://civil.colorado.edu/~balajir/Adv-Data-material/CVEN-6833/papers-2read/srinivas-hybrid-Jhydrology.pdf

Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

Failures in operation of water supply reservoir systems are often unavoidable during critical hydrologic periods. The failure characteristics of such systems can be represented by performance indicators such as reliability, resilience, and vulnerability. A mixed-integer programming model for the operation of a water supply reservoir during critical periods has been presented in the literature that incorporates these performance indicators. An improved formulation of this model that represents resilience more completely is discussed herein. In addition, a set of constraints with binary integer variables are included to account for reservoir spills. The improvements achieved with the modified model is demonstrated using the same example as presented with the original model.

K. Srinivasan

Papers

Performance‐based optimal design and rehabilitation of water distribution networks using life cycle costing

Investigation and comparison of sampling properties of L-moments and conventional moments

Multiobjective Optimal Waste Load Allocation Models for Rivers Using Nondominated Sorting Genetic Algorithm-II

Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

Mixed-Integer Programming Model for Reservoir Performance Optimization