A New Approach to Predict Daily pH in Rivers Based on the “à trous” Redundant Wavelet Transform Algorithm

Rajaee, Taher; Adamowski, Jan; Deo, Ravinesh C.

doi:10.1007/s11270-018-3715-3

Cited by 13 publications

(2 citation statements)

References 47 publications

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“…The technique of cross-correlation was employed to ascertain the temporal associations between the input variables and the data on water demand. This approach has been extensively employed in the determination of input delays (37)(38)(39)(40)(41)(42)(43). Fig.…”

Section: Resultsmentioning

confidence: 99%

Performance of TANN, NARX, and GMDHT Models for Urban Water Demand Forecasting: A Case Study in a Residential Complex in Qom, Iran

Rezaali,

Fouladi-Fard,

Karimi

2023

Avicenna J Environ Health Eng

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To keep the balance between demand and supply, methods based on the average per capita consumption were usually applied to predict water demand. More complicated models such as linear regression and time series models were developed for this purpose. However, after the introduction of artificial neural networks (ANNs), different applications of this method were used in the field of water supply management, especially for urban water demand prediction. In this study, multiple types of ANNs were studied to understand their suitability for a residential complex water demand prediction in the city of Qom, Iran. The results indicated that time series ANN (TANN), nonlinear autoregressive network with exogenous inputs (NARX), group method of data handling time series (GMDHT), and their wavelet counterparts (i.e., w-TANN and w-NARX) exhibited varying degrees of performance. Among the aforementioned models, w-NARX performed the best (based on the average overall error) with the test set root mean squared error (MSE) of 49.5 (m3 /h) and R of 0.93, followed by the GMDHT model with the test set MSE of 104 (m3 /h) and R of 0.97 and w-TANN with the test set MSE of 68.8 (m3 /h) and R of 0.91. In addition, the feedback connection in NARX compared to TANN demonstrated overall performance improvement.

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Section: Resultsmentioning

confidence: 99%

Performance of TANN, NARX, and GMDHT Models for Urban Water Demand Forecasting: A Case Study in a Residential Complex in Qom, Iran

Rezaali,

Fouladi-Fard,

Karimi

2023

Avicenna J Environ Health Eng

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show abstract

“…The forecast length in this review refers to the length of time to predict in advance. For example, if researchers used the historical data of the previous three days to predict the values of the current day, then the forecast length would be 1 [157]. However, 107 papers did not provide details about the forecast length which cast ambiguity and doubts to researchers in parameter settings [31].…”

Section: Resultsmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods

Peixoto,

da Costa Pereira,

Fernandes

et al. 2024

Environ Monit Assess

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A New Approach to Predict Daily pH in Rivers Based on the “à trous” Redundant Wavelet Transform Algorithm

Cited by 13 publications

References 47 publications

Performance of TANN, NARX, and GMDHT Models for Urban Water Demand Forecasting: A Case Study in a Residential Complex in Qom, Iran

Performance of TANN, NARX, and GMDHT Models for Urban Water Demand Forecasting: A Case Study in a Residential Complex in Qom, Iran

A Review of the Artificial Neural Network Models for Water Quality Prediction

Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods

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