Open AccessLinear programming Monte Carlo method based on remote sensing for ecological restoration of degraded ecosystem
Author(s) -
Ling Sun,
Zesheng Zhu
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/295/2/012041
Subject(s) - monte carlo method , ecosystem , environmental science , linear programming , ecology , remote sensing , computer science , geography , mathematics , statistics , biology , algorithm
A linear programming Monte Carlo method (LPMC) is proposed to achieve the optimal restoration of the whole degraded coastal wetland ecosystem, i. e. the minimum restoration cost, in the case of uncertain ecological restoration costs of different degraded coastal wetlands. LPMC can comprehensively and systematically complete the dynamic analysis of coastal wetland ecosystem restoration cost, including the uncertainty analysis of ecological restoration cost and the screening of equivalent robust solutions. The applicability of this method is demonstrated by solving a practical problem of ecological restoration of coastal wetlands. The results show that this method can generate the robust optimal solution block for the globally optimal objective function and decision variables under the condition of restoring cost uncertainty, including a variety of optimal solutions adapted to the different needs.