ANN-Based Estimation of Groundwater Quality Using a Wireless Water Quality Network

Kılıçaslan, Yılmaz; Tuna, Gürkan; Gezer, Gülsüm; Gülez, Kayhan; Arkoç, Orhan; Potirakis, Stelios M.

doi:10.1155/2014/458329

Cited by 17 publications

(8 citation statements)

References 10 publications

(15 reference statements)

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“…Then, they applied three typical MLP architectures to complete the Tur prediction whose inputs were NH 3 -N, EC, DO, pH, and WT. Klçaslan et al [128] randomly divided the datasets and pointed out that when the data tended to be roughly periodic after a year, the time length of data acquisition, covering a long period such as a year or more was highly recommended in order to capture long-term variation. Yang et al [129] found the most significant parameters by using analysis of variance (ANOVA) techniques.…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

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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Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

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

et al. 2020

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“…This technique, due to Levenberg [30] and Marquardt [31], is a combination of the following two methods [32,33]:…”

Section: Artificial Neural Network-based Control Of a Multiboat Groupmentioning

confidence: 99%

Section: Artificial Neural Network-based Control Of a Multiboat Groupmentioning

confidence: 99%

“…The optimization method known as Levenberg-Marquardt algorithm [29] was used for the development of the proposed NN. This technique, due to Levenberg [30] and Marquardt [31], is a combination of the following two methods [32, 33]: the Gauss-Newton's method approaches rapidly near to a global or local minimum but sometimes may deviate; the Gradient descent method certainly approaches through a proper selection of step size parameter but does slowly. …”

Section: Artificial Neural Network-based Control Of a Multiboat Groupmentioning

confidence: 99%

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ANN-Based Control of a Multiboat Group for the Deployment of an Underwater Sensor Network

Michailidis

Tuna

Gezer

et al. 2014

International Journal of Distributed Sensor Networks

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Underwater sensor networks (USNs) can be used for several types of commercial and noncommercial applications. However, some constraints resulting from the nature of aquatic environments severely limit their use. Due to constraints such as large propagation latency, low-bandwidth capacity, and short-distance communications, a large number of USN nodes are deployed to provide reliability in most applications. In this study, an unattended deployment approach based on the use of an autonomous boat group is proposed. A map of the deployment zone and optimal locations of USN nodes are fed into the onboard computers of the boat group. After processing these data and determining paths to be followed, the boat group deploys sensor nodes at predetermined locations. During the deployment, the boat group is controlled by an artificial neural network-(ANN-) based control system for reducing path errors. A set of performance evaluations is given to prove efficiency of the proposed control system. Performance results show that the boat group can successfully follow a predefined path set and deploy USN nodes. The tradeoffs between energy consumptions, end-to-end delay, and number of hops between underwater relay nodes of energy-efficient USN are also examined. The results indicate that increasing the number of hops reduces the total energy consumption and the end-to-end delay.

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“…With the developing of wireless sensor network technology, a variety of applications based WSN appear, such as land cover classification [1], SCR node detection in vehicular network [2], fault detection [3, 4], and groundwater quality estimation [5]. Traditionally, these applications analyze sample data in a fusion center [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Data Mining Based on Deep Neural Network for Wireless Sensor Network

Xie

Huang

et al. 2015

International Journal of Distributed Sensor Networks

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As the sample data of wireless sensor network (WSN) has increased rapidly with more and more sensors, a centralized data mining solution in a fusion center has encountered the challenges of reducing the fusion center's calculating load and saving the WSN's transmitting power consumption. Rising to these challenges, this paper proposes a distributed data mining method based on deep neural network (DNN), by dividing the deep neural network into different layers and putting them into sensors. By the proposed solution, the distributed data mining calculating units in WSN share much of fusion center's calculating burden. And the power consumption of transmitting the data processed by DNN is much less than transmitting the raw data. Also, a fault detection scenario is built to verify the validity of this method. Results show that the detection rate is 99%, and WSN shares 64.06% of the data mining calculating task with 58.31% reduction of power consumption.

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ANN-Based Estimation of Groundwater Quality Using a Wireless Water Quality Network

Cited by 17 publications

References 10 publications

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

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

ANN-Based Control of a Multiboat Group for the Deployment of an Underwater Sensor Network

Distributed Data Mining Based on Deep Neural Network for Wireless Sensor Network

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