Land use structure optimization based on uncertainty fractional joint probabilistic chance constraint programming

An uncertainty fractional joint probability chance constraint programming is developed to process land use structure optimization under uncertainty. The model integrate uncertainty programming into fractional programming, and the uncertainty programming include interval programming, fuzzy programmin...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Stochastic environmental research and risk assessment Jg. 34; H. 11; S. 1699 - 1712
Hauptverfasser: Gu, Jinjin, Zhang, Xiaorui, Xuan, Xiaodong, Cao, Yuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Springer Nature B.V
Schlagworte:
Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Economic models
Environment
Land pollution
Land use
Math. Appl. in Environmental Science
Mathematical programming
Optimization
Original Paper
Physics
Pollutants
Probability Theory and Stochastic Processes
Statistical analysis
Statistics for Engineering
Uncertainty
Waste Water Technology
Water Management
Water Pollution Control
Fractional joint probabilistic chance constraint programming
Land use structure optimization
Uncertainty
ISSN:1436-3240, 1436-3259
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:An uncertainty fractional joint probability chance constraint programming is developed to process land use structure optimization under uncertainty. The model integrate uncertainty programming into fractional programming, and the uncertainty programming include interval programming, fuzzy programming, stochastic programming and joint probability chance constraint programming. The results of the study are a series of land use policies in multiple scenarios with interval and deterministic numbers. The advantage of the model include it can (1) effectively integrate the two objectives of economic benefit maximization and pollution minimization by the fractional programming; (2) effectively process the uncertainty by the corresponding uncertainty programming; (3) reflect the impact of uncertainty on system benefit, pollutant discharge, and land use structure policy; and (4) develop a series of possible scenarios and corresponding feasible plans. The results of the study can help planners or decision makers develop flexible land use policy to address the multi-objective problems of maximum, minimum, and uncertainty. The proposed method is universal and can be extended to other cases.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-020-01841-w