Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network

The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learning algorithms is chosen for air pollution and water pollution cases. The purpose of this review is to reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose.

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“…al. ( 2022 ) used bidirectional GRU for the water quality prediction of Poyang Lake, China (Figs. 13 and 14 ), (Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

Nandi

Singh

Jain

et al. 2023

Int. J. Environ. Sci. Technol.

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“…Indeed, there are also cases where a new DNN model has been built by combining DNN with statistical techniques to predict algal blooms. A new model combining DNN, data decomposition, and fuzzy clustering was proposed to predict water quality factors influencing algal blooms [28]. An SAE-DNN model, combining the stacked autoencoder (SAE) technique with DNN, was developed to estimate the concentration of phycocyanin in cyanobacteria [29].…”

Section: Introductionmentioning

confidence: 99%

Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms

Kim

et al. 2023

Water

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Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model to predict algae conditions and respond to algal blooms through the HAB alert system. This study aimed to establish an additional deep learning model to effectively respond to algal blooms. The prediction model is based on a deep neural network (DNN), which is a type of artificial neural network widely used for HAB prediction. By applying the synthetic minority over-sampling technique (SMOTE) to resolve the imbalance in the data, the DNN model showed improved performance during validation for predicting the number of cyanobacteria cells. The R-squared increased from 0.7 to 0.78, MAE decreased from 0.7 to 0.6, and RMSE decreased from 0.9 to 0.7, indicating an enhancement in the model’s performance. Furthermore, regarding the HAB alert levels, the R-squared increased from 0.18 to 0.79, MAE decreased from 0.2 to 0.1, and RMSE decreased from 0.3 to 0.2, indicating improved performance as well. According to the results, the constructed data-based model reasonably predicted algae conditions in the summer when algal bloom-induced damage occurs and accurately predicted the HAB alert levels for immediate decision-making. The main objective of this study was to develop a new technology for predicting and managing HABs in river environments, aiming for a sustainable future for the aquatic ecosystem.

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“…Modeling and forecasting water quality and the parameters that influence it has been performed recently by Artificial Intelligence methods (Fuzzy techniques, ANFIS, C&RT) and hybrid method [ 14 , 18 , 19 , 20 ]. Water quality simulation and forecast utilizing exponential models, differential equations, deep learning neural networks, and fuzzy clustering have been developed by some scientists [ 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Efficiency of a Drinking Water Treatment Plant Using Statistical Methods and Quality Indices

Bărbulescu,

Barbeș

2023

Toxics

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This study presents the efficiency of a drinking water treatment plant from Constanța, Romania. Individual and aggregated indices are proposed and built using nine water parameters for this aim. The analysis of individual indices permits the detection of the period of malfunctioning of the water treatment plant with respect to various parameters at various sampling points. In contrast, the cumulated indices indicate the overall performance of the treatment plant during the study period, considering all water parameters. It was shown that the outliers significantly impact the values of some indices. Comparisons between the simple average and weighted average indices (built taking into account the importance of each parameter) better reflect the impact on the water quality of some chemical elements that might harm people’s health when improperly removed.

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Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network

Cited by 53 publications

References 62 publications

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms

Assessing the Efficiency of a Drinking Water Treatment Plant Using Statistical Methods and Quality Indices

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