Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Zaher Mundher Yaseen, Majeed Mattar Ramal, Lamine Diop, Othman Jaafar, Vahdettin Demir, Ozgur Kisi

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalWater Resources Management
DOIs
Publication statusAccepted/In press - 8 Feb 2018

Fingerprint

Water quality
water quality
Rivers
river water
Soft computing
fuzzy mathematics
Fuzzy logic
modeling
index
Fuzzy inference
expert system
stochasticity
Surface waters
river
Expert systems
river system
Redundancy
Sustainable development
Statistical methods
statistical analysis

Keywords

  • Hybrid ANFIS models
  • River sustainability
  • Tropical environment
  • Water quality index

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology

Cite this

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation. / Yaseen, Zaher Mundher; Ramal, Majeed Mattar; Diop, Lamine; Jaafar, Othman; Demir, Vahdettin; Kisi, Ozgur.

In: Water Resources Management, 08.02.2018, p. 1-19.

Research output: Contribution to journalArticle

Yaseen, Zaher Mundher ; Ramal, Majeed Mattar ; Diop, Lamine ; Jaafar, Othman ; Demir, Vahdettin ; Kisi, Ozgur. / Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation. In: Water Resources Management. 2018 ; pp. 1-19.
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