A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models

Trach, Roman; Trach, Yuliia; Kiersnowska, Agnieszka; Markiewicz, Anna; Lendo-Siwicka, Marzena; Rusakov, К. I.

doi:10.3390/su14095656

Cited by 33 publications

(8 citation statements)

References 70 publications

(79 reference statements)

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“…This can lead to better environmental management, improved regulatory compliance, and overall more effective wastewater treatment processes. A fuzzy logic model was employed [2], incorporating four input variables to compute a single output variable, namely Water Quality Index (WQI). Subsequently, in the concluding phase of the investigation, six distinct Arti cial Neural Network (ANN) models were scrutinized.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Framework for Prediction of Chemical Oxygen Demand in Wastewater Treatment Plant

Tuppad

2024

Preprint

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Determining treated effluent parameters in WWTPs is crucial for assessing operational efficiency, ensuring compliance with environmental regulations, and promoting sustainable and responsible wastewater treatment practices. Conventionally, standard laboratory analysis techniques are used to characterize the wastewater. Artificial Neural Network analysis is a promising tool to predict the wastewater characteristics, as a substitute to tedious laboratory techniques. They enable proactive decision-making and contributes to the overall effectiveness of the treatment processes. This paper constructs various regression models based on neural networks within the domain of traditional machine learning. The main objective of this paper is to design neural network-based regression models to predict Chemical Oxygen Demand (COD), a key parameter characterizing treated effluent of a domestic wastewater treatment plant. Date used in the study is the augmented data of influent and effluent characteristics values from a wastewater treatment plant. The strength of neural networks lies in their ability to capture complex, non-linear relationships in data and make them suitable for predicting wastewater parameters that may have intricate dependencies on input variables. In this paper, the KMeans cluster with Principal Component Analysis (PCA) is used for feature selection and anomaly detection to increase the performance of the regression model. Various algorithms used include Multilayer Perceptron Network, Polynomial Regression with a Neural Network, Generalized Additive Models (GAM) with a Neural Network, Radial Basis Function Network and Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) with different Hyperparameter methods for prediction. Based on the results, it can be concluded that Average of all five neural network models predicted COD value with Mean Square Error of 0.0015 and R-Square of 0.8522.

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Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Framework for Prediction of Chemical Oxygen Demand in Wastewater Treatment Plant

Tuppad

2024

Preprint

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“…Environmental forecasting, or anticipating future ecology qualities based on observable unanticipated factors is a valuable tool for management. Prediction provides managers with probability projections for probable water quality circumstances in a focus lake or reserve tank, enabling them to take pre-emptive management activities that decrease or prevent water degradation [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Water Quality Forecasting Reliability Through Optimal Parameterization of Neuro-Fuzzy Models via Tunicate Swarm Optimization

Kumar,

Godla

et al. 2024

IJACSA

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Forecasting water quality is critical to environmental management because it facilitates quick decisionmaking and resource allocation. On the opposite hand, current methods are not always able to produce reliable forecasts, which is often due to challenges in parameter optimization for complex models. This research presents a novel approach to enhance the forecasting accuracy of water quality by optimizing neuro-fuzzy models using Tunicate Swarm Optimisation (TSO). The introduction highlights the limitations of current techniques as well as the necessity for precise estimates of water quality. One of the drawbacks is that neuro-fuzzy models are not well-modelled, which makes it harder for them to identify the minute patterns in data on water quality. The suggested approach is unique in that it applies TSO, an optimization algorithm inspired by nature that emulates tunicates' behaviour, to the neuro-fuzzy models' parameter optimization process. The highly complex parameter space is effectively navigated by TSO's swarm intelligence, which strikes a balance between exploration and exploitation to improve model performance. To optimize model parameters, the process comprises three steps: creating an objective function, defining the neuro-fuzzy model, and seamlessly integrating TSO. By mimicking the motions of tunicates as they look for the best conditions in the marine environment, TSO constantly optimizes the variables. Experiments demonstrate that the proposed strategy is more effective than traditional optimization techniques in forecasting water quality. As seen by the optimised neuro-fuzzy model's increased prediction accuracy and several dataset validations, Tunicate Swarm Optimisation has potential for reliable environmental forecasting. This work presents a potential path for improved environmental decisionmaking systems by offering an optimisation strategy inspired by nature that overcomes the limitations of existing methods and enhances water quality forecasting tools.

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“…Surface water pollution has become a serious environmental issue on a global scale that needs to be continuously assessed and monitored at all scales to ensure the sustainability of ecosystems [1]. Surface water quality monitoring requires a thorough process that involves numerous chemical studies of water [2]. Throughout the world, river systems are significantly impacted by pollution.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Water Quality of the Keddara Dam (Algeria) Using Water Quality Indices

Fashagba,

Bessedik,

ElSayed

et al. 2024

Water

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Dams are regarded as crucial pieces of structure that store water for irrigation and municipal uses. Given their vital role, the dam’s water quality assessment is considered to be an important criterion and requires constant monitoring. In this research, we attempted to use two water quality indices (WQIs) methods to assess the water quality of the Keddara Dam, which is located on the Boudouaou River, Algeria, using eleven water quality parameters (temperature, pH, conductivity, turbidity, total suspended solids (TSS), full alkalimetric title (TAC), hydrometric title (TH), nitrite ions (NO2−), nitrate ions (NO3−), ammonium ions (NH4+), and phosphate ions (PO43−)) for data recorded from 29 December 2018 to 3 June 2021. Application of The Canadian Council of Ministers of the Environment (CCME) WQIs and the Weighted Arithmetic Method (WAM) indicated that the Keddara Dam’s water quality parameters were within the WHO’s permissible level, except for the conductivity and turbidity values. The results of the CCME WQI ranged from acceptable (81.92) to excellent (95.08) quality, whereas the WAM WQI ranged from 9.52 to 17.77, indicating excellent quality. This demonstrates that the Keddara Dam is appropriate for agriculture and municipal use. The water quality indices (WQIs) methods are recommended as valuable tools that allow both the public and decision-makers to comprehend and manage the water quality of any aquatic environment by providing flexibility in choosing variables.

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A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models

Cited by 33 publications

References 70 publications

An Artificial Neural Network Framework for Prediction of Chemical Oxygen Demand in Wastewater Treatment Plant

An Artificial Neural Network Framework for Prediction of Chemical Oxygen Demand in Wastewater Treatment Plant

Enhancing Water Quality Forecasting Reliability Through Optimal Parameterization of Neuro-Fuzzy Models via Tunicate Swarm Optimization

Evaluating the Water Quality of the Keddara Dam (Algeria) Using Water Quality Indices

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