Immediate water quality assessment in shrimp culture using fuzzy inference systems

Hernández, José Juan Carbajal; Sánchez-Fernández, Luis Pastor; Carrasco-Ochoa, Jesús Ariel; Martínez-Trinidad, José Fco.

doi:10.1016/j.eswa.2012.02.141

Cited by 63 publications

(45 citation statements)

References 15 publications

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“…FIS has been used in many applications since then. It has been applied in environmental models including: waste management (Vesely et al, 2016) forecasting air quality (Carbajal-Hernández et al, 2012a;Fisher, 2006;Sowlat et al, 2011) water quality (Carbajal-Hernández et al, 2012b;Che Osmi et al, 2016;Gharibi et al, 2012;Ocampo-Duque et al, 2006) models for performing risk assessment (Camastra et al, 2015;Jamshidi et al, 2013;Rodríguez et al, 2016) in the field of manufacturing and sales for supporting customers' requirements (Juang et al, 2007) forecasting automobile sales (Wang et al, 2011) stock price prediction (Chang & Liu, 2008) supplier selection (Lima Junior et al, 2013) measuring customer satisfaction (Zani et al, 2013). Similarly to our model, FIS has been applied in models for evaluating the performance level of several fields (Nadali et al, 2011;Nilashi et al, 2015).…”

Section: A Fuzzy Logic Approachmentioning

confidence: 99%

“…A combination of AHP and FIS has also been employed in several applications (Carreño Donevska et al, 2011;Nilashi et al, 2015;Rodríguez et al, 2016). A fuzzy logic inference system is a process from a given input of empirical values to an output including three main parts (Carbajal-Hernández et al, 2012b;Jamshidi et al, 2013;Ocampo-Duque et al, 2006;Ross, 2004): (1) membership functions; (2) fuzzy set operations; (3) IF-THEN inference rules. A Mamdani-type inference system (Mamdani & Assilian, 1975) is used in our model because of the more intuitive and human-like nature of its rules compared to other types (Che Osmi et al, 2016;Kovac et al, 2012).…”

Section: A Fuzzy Logic Approachmentioning

confidence: 99%

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A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

Oblak¹,

Kuzman²,

Grošelj³

2017

Istrazivanja I Projektovanja Za Privredu

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Section: A Fuzzy Logic Approachmentioning

confidence: 99%

Section: A Fuzzy Logic Approachmentioning

confidence: 99%

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

Oblak¹,

Kuzman²,

Grošelj³

2017

Istrazivanja I Projektovanja Za Privredu

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show abstract

“…The combination of the rules is called aggregation. The aggregation used to fuzzy union all output in the FIS is the maximum methods (Carbajal-Hernández et al, 2012). …”

Section: Rules 250mentioning

confidence: 99%

“…One of the Fuzzy's application approaches is Fuzzy Inference System and complex nature of assessing and managing the river basin. Study done by Carbajal-Hernández et al (2012) and Mahapatra et al (2011) has shown that number of water variables.…”

mentioning

confidence: 99%

“…That is to say, FIS is a process of formulating a mapping from a given multiple input to a single output using fuzzy logic. The process of fuzzy inference involves three important concepts: membership function, logical operations and If -Then rules (Carbajal-Hernández, Sánchez-Fernández, Carrasco-Ochoa, & Martínez-Trinidad, 2012;Ocampo-Duque, Osorio, Piamba, Schuhmacher, & Domingo, 2013) …”

mentioning

confidence: 99%

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Assessing Water Quality Index in River Basin : Fuzzy Inference System Approach

Lindang¹,

Tarmudi²,

Jawan³

2017

Malays. j. geosci.

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Freshwater shrimp farm monitoring system for Bangladesh based on internet of things

Uddin

Istiaq

Rasadin

et al. 2020

Engineering Reports

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The freshwater shrimp (Macrobrachium rosenbergii) fisheries sector is of vital significance to the economy of Bangladesh. This sector plays an important role in supplying nutrition, employment creation, poverty alleviation, and overseas currency acquisition. Rapid population growth, environmental pollution, and global warming are reducing the production level of the fisheries (freshwater shrimp) sector. There are various environmental metrics, such as acidity and salinity level of water, turbidity, dissolved oxygen level, and so on, which play a vital role in the effective production of shrimps. It is hence paramount environmental and production parameters be constantly monitored to ensure quality in the production chain. Nevertheless, old-fashioned (ie, manual) monitoring systems are cumbersome, time-consuming, and never performed in real time. Therefore, a constant and computerized monitoring system is the only way to handle those metrics efficiently and in real time. In order to address these problems, a real-time freshwater shrimp farm monitoring system is presented in this article. This monitoring system integrates technologies such as microcontroller-based physical devices, internet of things, and web applications, that allow users to remotely monitor a shrimp farm, as well as to receive alerts when an out-of-range water parameter (ie, temperature, pH, dissolved oxygen, salinity level, and turbidity of water) is detected. The physical implementation of this system consists of a set of sensors that allow for collecting data about the water metrics of shrimp farm. This system was evaluated to test its effectiveness in terms of the size, weight, and the percentage of survival of the shrimp achieved when the shrimp culture pond is monitored by this system.

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Immediate water quality assessment in shrimp culture using fuzzy inference systems

Cited by 63 publications

References 15 publications

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

Assessing Water Quality Index in River Basin : Fuzzy Inference System Approach

Freshwater shrimp farm monitoring system for Bangladesh based on internet of things

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