Development of a water quality index using a fuzzy logic: A case study for the Sorocaba river

Roveda, Sandra Regina Monteiro Masalskiene; Bondanca, Ana Paula Maia; Silva, Joao Guilherme Soares; Roveda, José Arnaldo Frutuoso; Rosa, André Henrique

doi:10.1109/fuzzy.2010.5584172

Cited by 13 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was identified that the fuzzy application avoided the attenuation that variables classified in "critical conditions" suffer under the influence of other variables classified in "good conditions." Similar results were obtained analyzing fuzzy application in Water Quality Index [91] and Soil Quality Index [92].…”

Section: How Does the Feedback Between The Stages Of The Open-minded Roadmap Support The Decision Making Process?supporting

confidence: 82%

New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

Silva¹,

Matulovic

Manzione

2021

SN Appl. Sci.

View full text Add to dashboard Cite

Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

show abstract

Section: How Does the Feedback Between The Stages Of The Open-minded Roadmap Support The Decision Making Process?supporting

confidence: 82%

New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

Silva¹,

Matulovic

Manzione

2021

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Advantage of this model is that it can be applied to any of locality with the proper selection of characteristic parameters for the site. Many authors have used fuzzy logic for assessing water quality [1][2][3]5,[26][27][28][29]. Advantages and disadvantages of fuzzy models for assessing water quality are presented in the incoming Table 5.…”

Section: Consumption Is Not Acceptable Then Gwq Of Group 1 Is Acceptable Rule 2: If Ph Is Acceptable and Kmno 4 Consumption Is Not Acceptmentioning

confidence: 99%

“…Fuzzy logic provides a framework to model uncertainty, the human way of thinking, reasoning and perception process [2]. The results on water quality obtained using the index developed on the basis of fuzzy set theory were found to be more useful than those derived from the Water Quality Index method that is currently used [3].…”

mentioning

confidence: 99%

Fuzzy model for determination and assessment of groundwater quality in the city of Zrenjanin, Serbia

Kiurski-Milosević

Vojinovic-Miloradov

Ralević

2015

Hem Ind

View full text Add to dashboard Cite

The application of the fuzzy logic for determination and assessment of the chemical quality of groundwater for drinking purposes in the city of Zrenjanin is presented. The degree of certainty and uncertainties are one of the problems in the most commonly used methods for assessing the water quality. Fuzzy logic can successfully handle these problems. Evaluation of fuzzy model was carried out on the samples from two representative wells that are located at depths of two aquifers from which water is taken to supply the population as drinking water. The samples were analyzed on 8 different chemical water quality parameters. In the research arsenic concentration (As3+, As5+) is considered as the dominant parameter due to its suspecting carcinogenic effects on human health. This type of research is for the first time conducted in the city of Zrenjanin, middle Banat region. [Projekat Ministarstva nauke Republike Srbije, br. MNTR174009 i br. TR34014]

show abstract

“…The results indicated that the method solves the uncertainty problem of the change of the WQ. A WQ index using a fuzzy inference system has been successfully proposed, since it has proven to be advantageous in addressing uncertainty problems [27].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fuzzy Wavelet Neural Network Model with Self‐Adapted Fuzzy c‐Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers

et al. 2018

View full text Add to dashboard Cite

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network (FWNN) including the neural network (NN), the fuzzy logic (FL), the wavelet transform (WT), and the genetic algorithm (GA) was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network (FNN), the wavelet neural network (WNN), and the neural network (ANN). The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river.

show abstract

Development of a water quality index using a fuzzy logic: A case study for the Sorocaba river

Cited by 13 publications

References 5 publications

New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

Fuzzy model for determination and assessment of groundwater quality in the city of Zrenjanin, Serbia

A Hybrid Fuzzy Wavelet Neural Network Model with Self‐Adapted Fuzzy c‐Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers

Contact Info

Product

Resources

About