Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model

Ravansalar, Masoud

doi:10.1007/s10661-015-4590-7

Cited by 46 publications

(26 citation statements)

References 44 publications

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“…The most common activation functions are the rectified linear unit (relu), sigmoid, and tanh. In modeling, sigmoid activation function is the most common function [22]. However, for this study, the rectified linear unit was selected for its better performance than the others.…”

Section: The Multilayer Perceptron For Groundwater Level Modelingmentioning

confidence: 99%

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Afzaal

Farooque

Abbas

et al. 2019

Water

102

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Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada. Variables including stream level, stream flow, precipitation, relative humidity, mean temperature, evapotranspiration, heat degree days, dew point temperature, and evapotranspiration for the 2011-2017 period were used as input variables. Using a hit and trial approach and various hyperparameters, all ANNs were trained from scratched (2011)(2012)(2013)(2014)(2015) and validated (2016)(2017). The stream level was the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds (R 2 = 50.8 and 49.1%, respectively). The MLP performed better in validation for Baltic River and Long Creek watersheds (RMSE = 0.471 and 1.15, respectively). Increased number of variables from 1 to 4 improved the RMSE for the Baltic River watershed by 11% and for the Long Creek watershed by 1.6%. The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management.Water 2020, 12, 5 2 of 18 procedure that requires thorough knowledge of physical hydrological parameters, big data, hydrological models, model inputs, and the geometry of watersheds [3]. Aspects of hydrogeology-i.e., geological factors affecting the distribution and movement of groundwater underneath the soil surface-need to be properly understood when modeling GWLs and manipulating the modeling results. Watershed scale fluctuations in GWLs occur over a period of several decades, and the resulting cumulative effects on streamflow depletion may not be fully realized for years [4]. Resultantly, depending upon the distance of the pumping station from the stream and the geologic characteristics of the aquifer, the groundwater system may take decades to recover from streamflow depletion caused by intermittent pumping. Components of the surface-and the sub-surface physical hydrology of a watershed-i.e., streamflow and groundwater flow, respectively-are interconnected, making the stream-aquifer interaction one of the key processes governing the groundwater flow pattern in a watershed [1]. Groundwater fluctuations affect streamflow and vice versa, as the pumping wells capture groundwater that would otherwise discharge to connected streams, rivers, and other surface-water bodies [4]. Francis [5] reported that, in typical watersheds of Prince Edward Island, the base flow represents almost 80% of the streamflow in the late summer and fall months. Stream length in these island watersheds ranges from less than 1 km to 2...

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Section: The Multilayer Perceptron For Groundwater Level Modelingmentioning

confidence: 99%

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Afzaal

Farooque

Abbas

et al. 2019

Water

102

View full text Add to dashboard Cite

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“…ANNs have been used to simulate the effect of climate change on discharge and the export of dissolved organic carbon and nitrogen from river basins (Clair and Ehrman 1996), predict salinity of groundwater and rivers (Maier and Dandy 1996;Huang and Foo 2002;Nasr and Zahran 2014;Ravansalar and Rajaee 2015), simulate and forecast residual chlorine concentrations within urban water systems (Rodriguez and Serodes 1998), prediction of arsenic, sulfate and nitrate concentrations in groundwater (Yesilnacar et al 2008;Yesilnacar et al 2012;Chang et al 2010), spatial distribution of groundwater quality (Khashei-Siuki and Sarbazi 2013) and determine the leachate amount from municipal solid waste landfill (Karaca and Ozkaya 2006).…”

Section: Introductionmentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

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Salinity is one of the main factors in groundwater quality monitoring. The main objectives of this study are to investigate and compare the accuracy of three different neural computing techniques, multi-layer perceptron neural network (MLP), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN), in prediction of groundwater salinity of the Tabriz plain confined aquifer, expressed by electrical conductivity [EC (lS/cm)], and to employ an integrated method to combine the advantages of neural network models utilizing the concept of committee machine. To develop the models, 93 data records of groundwater samples were collected from East Azarbaijan regional water company. The data set including Ca 2? , Mg 2? , Na ? , SO 4 2-and Cl -concentrations as the inputs and salinity [EC (lS/ cm)] as an output were divided into two subsets; training and testing based on cross validation approach. After training and testing of the models, the performance of the models were evaluated using root mean square errors (RMSE), determination coefficient (R 2 ) and mean absolute error (MAE). The performance criteria of the constructed neural network models showed that RBFNN model has the best performance in predicting salinity. The committee neural network (CNN) combined the results of salinity predicted from MLP, RBFNN and GRNN, each of them has a weight factor showing its contribution in overall prediction. The optimal weights were derived by a genetic algorithm (GA). The results of salinity prediction derived from CNN showed that the CNN performs better than any one of the individual ANNs acting alone for predicting groundwater salinity.

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“…In this review, data-intensive approaches are to combine different technologies to preprocess the data. Wavelet analysis approaches such as WANN [72] can provide some useful information about the physical structure of the data. ANNs models the approximation and details component from the discrete wavelet transformation (see Figure 4).…”

Section: Hybrid Architecturesmentioning

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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Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model

Cited by 46 publications

References 44 publications

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

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

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