Diagnosing reservoir water quality using self-organizing maps and fuzzy theory

被引:132
|
作者
Lu, RS [1 ]
Lo, SL [1 ]
机构
[1] Natl Taiwan Univ, Grad Inst Environm Engn, Taipei 106, Taiwan
关键词
self-organizing map (SOM); eutrophication; reservoir water quality; fuzzy theory;
D O I
10.1016/S0043-1354(01)00449-3
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Since trophic status assessment of water quality is very important for the water resources management, the assessment results obtained from using only one parameter may easily mislead or bias the decision makers or managers. Even when using a multivariable index, how to determine the weights of all factors is debatable. In this research, one complementary evaluation method, self-organizing map (SOM), for diagnosing water quality has been used to develop a trophic state classifier and is illustrated with a case study of trophic status assessment for Fei-Tsui Reservoir in Taiwan. The historical database was collected from the management agency of Fei-Tsui Reservoir from 1987 to 1995. The results of SOM are compared with those of the Carlson index and Fuzzy synthetic evaluation, showing that the inconsistent records can be mapped to the conflicting data zone of the SOM output map. In addition, SOM creates a diagnostic axis on the map to express the trophic status of the water body. As long as the SOM model is well-trained, new records can be assessed and classified as either one of three trophic levels or special cases. If special water quality conditions are expressed on the SOM output, those data can reveal that either total phosphorus (TP) or chlorophyll A (chl a) is higher than usual. (C) 2002 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:2265 / 2274
页数:10
相关论文
共 50 条
  • [41] Relational Fuzzy Self-Organizing Maps for Cluster Visualization and Summarization
    Khalilia, Mohammed A.
    Popescu, Mihail
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2014, 22 (06) : 913 - 940
  • [42] THE SELF-ORGANIZING FEATURE MAPS
    KOHONEN, T
    MAKISARA, K
    PHYSICA SCRIPTA, 1989, 39 (01): : 168 - 172
  • [43] Decentralizing Self-organizing Maps
    Khan, Md Mohiuddin
    Kasmarik, Kathryn
    Garratt, Matt
    AI 2021: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, 13151 : 480 - 493
  • [44] SELF-ORGANIZING SEMANTIC MAPS
    RITTER, H
    KOHONEN, T
    BIOLOGICAL CYBERNETICS, 1989, 61 (04) : 241 - 254
  • [45] Recursive self-organizing maps
    Voegtlin, T
    NEURAL NETWORKS, 2002, 15 (8-9) : 979 - 991
  • [46] Recursive self-organizing maps
    Voegtlin, T
    Dominey, PF
    ADVANCES IN SELF-ORGANISING MAPS, 2001, : 210 - 215
  • [47] Robust self-organizing maps
    Allende, H
    Moreno, S
    Rogel, C
    Salas, R
    PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, 2004, 3287 : 179 - 186
  • [48] Self-organizing maps and SVD
    Dvorsky, Jiri
    DEXA 2007: 18TH INTERNATIONAL CONFERENCE ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2007, : 143 - 147
  • [49] Extensions of self-organizing maps
    Trutschl, M
    Cvek, U
    ISIS International Symposium on Interdisciplinary Science, 2005, 755 : 204 - 214
  • [50] Self-organizing visual maps
    Sim, R
    Dudek, G
    PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2004, : 470 - 475