Evaluation of pool water quality of trout farms by fuzzy logic: monitoring of pool water quality for trout farms

Yalçuk, Arda; Postalcioglu, Seda

doi:10.1007/s13762-014-0536-9

Cited by 22 publications

(12 citation statements)

References 18 publications

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“…However, for a variety of reasons, the actual data is usually incomplete. Therefore, the study of the classifiers for incomplete data is of great significance [1][2][3][4]. The current classification method commonly used decision tree, neural network, KNN, SVM and the Bayesian methods, KNN algorithm in classification accuracy is simple and high in the Chinese automatic text classification has been applied widely.…”

Section: The Data Classification Methodologymentioning

confidence: 99%

Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis

Wang¹,

Duoyong²,

Xu³

2017

IJDTA

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In this paper, we conduct research on environmental quality evaluation model based on data mining and correlation analysis. Along with the application of multi-statistical analysis method, the big data analysis law by has been applied in the environmental quality evaluation. Reciprocities of this method among from many targets starts that changes into a few not related overall targets many targets and the merit lies in had considered the relevance among various targets that can maximum limit retain original information, carries on best comprehensive dimensionality reduction processing to the high dimensional data. Aside by using this feature, this paper proposes the data mining and correlation analysis based model. The basic task of the analytical grey incidence is the microscopic or macroscopic geometry of behavior based factor sequence is close, toanalyze and contribution degree of influence or the factor between determination factors to main behavior, but the gray incidence space carries on the foundation of analytical grey incidence. We implement the model on the air and water quality evaluation which are assisted with the neural network and gray analysis. The experimental result reflect the effectiveness of our model, it can evaluate the environmental quality effectively.

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Section: The Data Classification Methodologymentioning

confidence: 99%

Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis

Wang¹,

Duoyong²,

Xu³

2017

IJDTA

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“…The underwater quality monitoring applications vary from monitoring water quality of canals to oceans. The authors in [9] have developed an application to monitor the quality of pool water for trout farms. For the growth of trout in a farm/pool, various parameters were monitored such as chemical oxygen demand, ammonium nitrogen (NH 3 -N), pH, and electrical conductivity (EC).…”

Section: Water Qualitymentioning

confidence: 99%

“…The authors devised and proposed a group based wireless sensor network for accurate quantitative analyses for monitoring water pollution. The authors in [9] developed, monitored, and maintained the ecosystem for trout in trout farms by sensing the quality of water.…”

Section: 12mentioning

confidence: 99%

Underwater Sensor Network Applications: A Comprehensive Survey

Felemban

Shaikh

Qureshi

et al. 2015

International Journal of Distributed Sensor Networks

317

190

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There is no escaping fact that a huge amount of unexploited resources lies underwater which covers almost 70% of the Earth. Yet, the aquatic world has mainly been unaffected by the recent advances in the area of wireless sensor networks (WSNs) and their pervasive penetration in modern day research and industrial development. The current pace of research in the area of underwater sensor networks (UWSNs) is slow due to the difficulties arising in transferring the state-of-the-art WSNs to their underwater equivalent. Maximum underwater deployments rely on acoustics for enabling communication combined with special sensors having the capacity to take on harsh environment of the oceans. However, sensing and subsequent transmission tend to vary as per different subsea environments; for example, deep sea exploration requires altogether a different approach for communication as compared to shallow water communication. This paper particularly focuses on comprehensively gathering most recent developments in UWSN applications and their deployments. We have classified the underwater applications into five main classes, namely, monitoring, disaster, military, navigation, and sports, to cover the large spectrum of UWSN. The applications are further divided into relevant subclasses. We have also shown the challenges and opportunities faced by recent deployments of UWSN.

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“…Tuna et al [10] develop two underwater WSN based approaches to monitor the water quality at drinking water reservoirs using sensors with different communication interfaces. Yalcuk et al [11] develop a fuzzy logic based application to monitor the quality of pool water for trout farms. In addition, some monitoring strategies have been also discussed in the literature.…”

Section: Water Quality Monitoringmentioning

confidence: 99%

On Cost-Efficient Sensor Placement for Contaminant Detection in Water Distribution Systems

Zeng

et al. 2016

IEEE Trans. Ind. Inf.

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In recent years, water pollution or contamination incidents happened frequently, causing serious disasters and negative social impact. To reduce the water contamination risk, water quality monitoring sensors should be deployed in water distribution system to enable real-time pollution detection. It is desirable to deploy sensors everywhere so that any contamination event can be detected and reported in a timely manner. Unfortunately, this is a luxury and unrealistic vision because of high deployment cost. It is significant to lower the deployment cost provided that the quality-of-sensing, e.g., coverage and contamination detection time, can be guaranteed for effective depollution action. In this paper, we consider a water quality monitoring sensor network consisting of two kinds of sensors with different prices. The expensive one is of cellular communication capability and therefore is able to send sensing information to control center directly, while, the cheaper one is of only sensor-to-sensor communication capability. We investigate a cost-efficient sensor deployment problem on how to deploy these two kinds of sensors in a given water distribution system to minimize the deployment cost, without violating the quality-of-sensing requirement. We first formulate the problem into a Mixed Integer Quadratically Constrained Programming (MIQCP) problem, which is then linearized into an equivalent Mixed Integer Linear Programming (MILP). We further propose a polynomial two-stage heuristic algorithm and evaluate its efficiency via extensive simulation-based studies.

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Evaluation of pool water quality of trout farms by fuzzy logic: monitoring of pool water quality for trout farms

Abstract: Pool water must have a certain quality parameters for the vital processes of trout. For this

Cited by 22 publications

References 18 publications

Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis

Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis

Underwater Sensor Network Applications: A Comprehensive Survey

On Cost-Efficient Sensor Placement for Contaminant Detection in Water Distribution Systems

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