| Literature DB >> 29801224 |
Mengdi Li1, Juntao Fan2, Yuan Zhang1, Fen Guo1, Lusan Liu1, Rui Xia1, Zongxue Xu3, Fengchang Wu1.
Abstract
Aiming to protect freshwater ecosystems, river ecological restoration has been brought into the research spotlight. However, it is challenging for decision makers to set appropriate objectives and select a combination of rehabilitation acts from numerous possible solutions to meet ecological, economic, and social demands. In this study, we developed a systematic approach to help make an optimal strategy for watershed restoration, which incorporated ecological security assessment and multi-objectives optimization (MOO) into the planning process to enhance restoration efficiency and effectiveness. The river ecological security status was evaluated by using a pressure-state-function-response (PSFR) assessment framework, and MOO was achieved by searching for the Pareto optimal solutions via Non-dominated Sorting Genetic Algorithm II (NSGA-II) to balance tradeoffs between different objectives. Further, we clustered the searched solutions into three types in terms of different optimized objective function values in order to provide insightful information for decision makers. The proposed method was applied in an example rehabilitation project in the Taizi River Basin in northern China. The MOO result in the Taizi River presented a set of Pareto optimal solutions that were classified into three types: I - high ecological improvement, high cost and high benefits solution; II - medial ecological improvement, medial cost and medial economic benefits solution; III - low ecological improvement, low cost and low economic benefits solution. The proposed systematic approach in our study can enhance the effectiveness of riverine ecological restoration project and could provide valuable reference for other ecological restoration planning.Keywords: Ecological security; Multi-objective optimization; Non-dominated Sorting Genetic Algorithm II; Rehabilitation; River restoration
Year: 2018 PMID: 29801224 DOI: 10.1016/j.scitotenv.2018.04.411
Source DB: PubMed Journal: Sci Total Environ ISSN: 0048-9697 Impact factor: 7.963