Development of river water quality management using fuzzy techniques: a review

Osmi, Siti Fatimah Che; Malek, Marlinda Abdul; Yusoff, Marina; Azman, N. H.; Faizal, Wan Mohd.

doi:10.1080/15715124.2015.1105232

Cited by 24 publications

(8 citation statements)

References 42 publications

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“…The basis of Fuzzy logic is the theory of the set of Fuzzy [28]. Fuzzy logic has been proven to be able to solve problems of measuring water quality, especially when dealing with the uncertainty of data and ambiguity in river water systems [29]. The results of research conducted by [30] states that the Fuzzy logic method is a feasible method to be used in determining water quality.…”

Section: Theoretical Basismentioning

confidence: 99%

Microcontroller Based Water Measurement Level Prototype Using Fuzzy Logic Method

Dalle¹,

Elfirman²,

Sufyan³

2020

TEM Journal

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Water is the source of life for humans. Its sustainability has to be maintained because water’s quality will affect human life. One of the water sources that are usually used by people is from the local water Company (PDAM) in every city in Indonesia. One of the PDAM Companies in the Marabahan City, South Kalimantan, is treating water from the Jejangkit River, but that water is not always clean; it can be murkier in a rainy season and tidal conditions. That is why the water quality condition in that river needs to be known. In that case, this study aims at designing a prototype of microcontroller-based turbidity of water detector by implementing the fuzzy logic. It can send a real-time report where later it will be used by PDAM in Marabahan City. The stages of turbidity measurement consist of start, initialization of I/O, turbidity sensor of reading object, fuzzy logic, read the turbidity level, stop. The result of this study is that the developed prototype is able to read the turbidity level of water and it can send a real-time report. It is recommended to develop the further turbidity sensor by using other components that are not only able to read the turbidity level, but they are also able to read the pH value and can be developed with other methods, in order that it can read the sharper data accuracy.

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Section: Theoretical Basismentioning

confidence: 99%

Microcontroller Based Water Measurement Level Prototype Using Fuzzy Logic Method

Dalle¹,

Elfirman²,

Sufyan³

2020

TEM Journal

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“…and non-point (urban and rural runoff, etc.) sources are equally important for sustainable management of water resources [8,22,60]. In order to achieve good water quality in rivers, the point sources should be identified and the problematic or ineffective sewage treatment plants should be located and upgraded [61].…”

Section: Temporal Correlations Between Heavily Polluted Surface Watermentioning

confidence: 99%

“…Clustering algorithms, as established unsupervised machine learning models, have been used to analyze data from a wide range of disciplines, such as gene expression data in biology and stock market financial data [18,19], yet have been rarely applied to the water environment due to the lack of data [11,[20][21][22][23]. The partition-based, hierarchical, and density-based algorithms are all popular spatial clustering methods [24].…”

Section: Introductionmentioning

confidence: 99%

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Miao

Guo

et al. 2019

Water

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Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

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“…In the next two stages the fuzzy logic inference system (Carbajal-Hernández et al, 2012b;Jamshidi et al, 2013;Ocampo-Duque et al, 2006;Ross, 2004) was shaped. The Mamdani-type inference system (Mamdani and Assilian, 1975) was used due to its intuitive rules and better suitability to human-like nature (Che Osmi et al, 2016;Kovac et al, 2012). Accordingly, we first defined the membership functions of all criteria.…”

Section: Fuzzy Logic Model Theorymentioning

confidence: 99%

“…Similar to the earlier introduced models, these also produce outcomes in the form of numeric ranges (an arbitrary numeric range or the range between 0 and 1) that can be transformed into linguistic variables which are understood by non-specialists (Silvert, 2000). Such models are currently being used to evaluate environmental properties and quality (Peche and Rodríguez, 2012), water quality and management (Carbajal-Hernández et al, 2012b;Che Osmi et al, 2016;Gharibi et al, 2012;Lermontov et al, 2009;Mahapatra et al, 2011;Ocampo-Duque et al, 2006;Yan et al, 2010), forest conditions (Ochoa-Gaona et al, 2010), habitat quality (Mocq et al, 2013), decision support in ecosystem management (Adriaenssens et al, 2004) and exploration of population ecology (Kampichler et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Toward objective assessment of the conservation status of (the Natura 2000) forest habitat types: A comparison of a qualitative and a quantitative modeling approach

Kovač

Grošelj

2018

Ecological Indicators

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To halt the loss of biodiversity in natural habitats, the EU passed the Habitats Directive and established the Natura 2000 network. The network captured forest habitats and habitat types, whose conservation status must be reported under Article 17 of the Directive. Hence, the harmonization of habitat assessment methods and reporting formats are a critical issue. So far, the EU Commission and Member States have managed to design and issue reporting guidelines. However, as many of these reports are largely based on expert opinions, they tend to be biased and incomparable. To make conservation status assessments and reporting more consistent, this study evaluated a set of indicators with two decision support models. The DEX model operated with linguistic statements derived from numerical values, while the fuzzy model utilized numeric input. Both models were tested with data from Slovenia's three largest forest habitat types (FHT), namely 9110 -Luzulo-Fagetum beech forests, 91K0 -Illyrian Fagus sylvatica forests (Aremonio-Fagion) and 91L0 -Illyrian oak-hornbeam forests (Erythronio-Carpinion), provided by the Forest and Forest Ecosystem Condition Survey. The DEX model produced uniform results and defined the conservation status of all three FHTs as least favorable. Conversely, the fuzzy model produced three different conservation status grades for the FHTs: a favorable conservation status for the 91K0 FHT, least favorable for the 9110 FHT and unfavorable for the 91L0 FHT. Its results were logical and in accord with the existing assessments. The study showed that both models could be used for the evaluation of FHT traits. However, because of its larger sensitivity, the fuzzy model allowed detecting subtle differences among the indicator values due to the use of continuous numerical data and more sophisticated mathematical procedures.

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Development of river water quality management using fuzzy techniques: a review

Cited by 24 publications

References 42 publications

Microcontroller Based Water Measurement Level Prototype Using Fuzzy Logic Method

Microcontroller Based Water Measurement Level Prototype Using Fuzzy Logic Method

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Toward objective assessment of the conservation status of (the Natura 2000) forest habitat types: A comparison of a qualitative and a quantitative modeling approach

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