An adaptive ant colony optimization framework for scheduling environmental flow management alternatives under varied environmental water availability conditions

Abstract

Human water use is increasing and, as such, water for the environment is limited and needs to be managed efficiently. One method for achieving this is the scheduling of environmental flow management alternatives (EFMAs) (e.g., releases, wetland regulators), with these schedules generally developed over a number of years. However, the availability of environmental water changes annually as a result of natural variability (e.g., drought, wet years). To incorporate this variation and schedule EFMAs in a operational setting, a previously formulated multiobjective optimization approach for EFMA schedule development used for long-term planning has been modified and incorporated into an adaptive framework. As part of this approach, optimal schedules are updated at regular intervals during the planning horizon based on environmental water allocation forecasts, which are obtained using artificial neural networks. In addition, the changes between current and updated schedules can be minimized to reduce any disruptions to longterm planning. The utility of the approach is assessed by applying it to an 89km section of the River Murray in South Australia. Results indicate that the approach is beneficial under a range of hydrological conditions and an improved ecological response is obtained in a operational setting compared with previous longterm approaches. Also, it successfully produces trade-offs between the number of disruptions to schedules and the ecological response, with results suggesting that ecological response increases with minimal alterations required to existing schedules. Overall, the results indicate that the information obtained using the proposed approach potentially aides managers in the efficient management of environmental water.

Systems modeling to improve the hydro‐ecological performance of diked wetlands

“…In scenarios 1 and 2 with optimized management and unlimited gate changes, the low shadow values associated with the vegetation management budget constraint and small increase in wetland performance when no Phragmites was present initially suggest that there may be little value to explicitly represent vegetation in the systems model. In other words, one could adequately define ecological objectives from only flow variables as in prior systems modeling studies [Cardwell et al, 1996;Higgins et al, 2011;Loucks, 2006;Steinschneider et al, 2013;Stralberg et al, 2009;Szemis et al, 2014]. In these scenarios, Phragmites cover had a seemingly small influence because cover was low relative to managers' target of 10% cover and habitat suitability of vegetation stayed at or close to a value of 1 (excellent).…”

“…Szemis et al . [] used ant colony optimization to identify environmental flows in the Murray basin that maximize ecological scores for indicator species in wetland and floodplain areas. And in the Connecticut River basin, Steinschneider et al .…”

“…A nonlinear integer programming model recommended investments in flow control structures to minimize changes of the natural flow regime in the Murray River, Australia [Higgins et al, 2011]. Szemis et al [2014] used ant colony optimization to identify environmental flows in the Murray basin that maximize ecological scores for indicator species in wetland and floodplain areas. And in the Connecticut River basin, Steinschneider et al [2013] used penalty-based linear programming to minimize the departure of reservoir storage levels, releases, and instream flows from target values established to generate hydropower, supply water, and maintain aquatic fish and invertebrate habitat.…”

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

“…Examples of such approaches are presented by Walters and Lohbeck (1993), Walters and Smith (1995) and Savic and Walters (1995). Alternatively, ant colony optimisation algorithms can be used to dynamically reduce the size of the search space during the optimisation using if-then rules for problems that can be represented in the form of a decision tree, where the selection of particular decision variable values affects the availability / feasibility of decision variable options at subsequent steps in the decision-making process (Foong et al, 2008a;Foong et al, 2008b;Szemis et al, 2013;Szemis et al, 2012;Szemis et al, 2014).…”

Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty

“…It follows that an adaptive approach to the optimal sequencing of urban water supply augmentation options is not simply a matter of reapplying an optimal static approach over a sliding window [see Szemis et al ., ], but requires careful design so that it enables the identification of (i) augmentation sequences that are both optimal for the long term, yet sufficiently flexible to be able to be adapted with minimal loss of optimality and (ii) augmentation options that are robust to changing conditions in periods between the implementation of augmentation options. In other words, such an approach should account for (i) dynamic robustness over the entire planning horizon, (ii) static robustness during those periods of the planning horizon when no changes can be made to the system, and (iii) pathways that are sufficiently flexible to cater to adaptation at minimal loss of optimality.…”