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Subject: ARIMA model ×

Publication Search Results

Now showing 1 - 3 of 3
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    Monitoring of an aquatic environment in aquaculture using a MEWMA chart
    (Elsevier, 2019-01-18) Mulema, S. A.; Carrión García, Andrés; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Gestión de la Calidad y del Cambio
    [EN] Monitoring an aquatic environment in aquaculture is usually carried out by controlling its physicochemical and biological parameters. The global state of the process is evaluated though the individual conditions shown in each parameter. However, the correlation structure in the data, caused by interdependence between these parameters, provides relevant information which must be controlled. Thus, the complex structure of the data requires a multivariate control method to explore both the individual and interactive effects of the environmental parameters. To achieve this goal, this paper presents a statistical method based on a Multivariate Exponentially Moving Average (MEWMA) chart to monitor the multivariate structure of an aquatic environment in aquaculture, especially in a crop of tilapia in Mozambique. The results show that the adjusted MEWMA chart is more consistent when controlling the conformity and stability of environmental parameters. Therefore, it can be considered an efficient statistical method to monitor an aquatic environment in aquaculture.
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    Quality and productivity in aquaculture: Prediction of oreochromis mossambicus growth using a transfer function ARIMA model
    (University of Kragujevac, Center of Quality, 2018) Mulema, Sérgio Afonso; Carrión García, Andrés; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Gestión de la Calidad y del Cambio; Universidade Pedagógica de Moçambique
    [EN] The cultivation of aquatic species in aquaculture aims at improving fish production, making the activity more dependent on human capacity, especially with what concerns to the application of efficient methods for its management. The weight and standard length are the biometric parameters mostly used to control the growth of tilapia in the cultivation process. In this work there is presented a statistical method based on transfer function ARIMA model to adjust and predict the weight of tilapia with the support of the values of the standard length. The model was adjusted to the data recorded in 21 weeks in the tilapia growing tank of the company Aquapesca de Mocambique, and the results showed that there was a good adjustment (with an explained variability of 97.8%) with which the model can be used to predict the future values of tilapia weight and make the activity more controllable and profitable.
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    Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study
    (Springer Verlag, 2011-03) García Díaz, Juan Carlos; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Universitat Politècnica de València
    Contaminated water resources have important implications on health and the environment. Nitrate contamination of the groundwater is a serious problem in the European Union. A method based on the statistical process control (SPC) and time series analysis is developed to monitoring and to predict the concentration evolution of nitrate (NO 3 -) in groundwater. In many pumping wells the NO 3 -concentration ([NO 3 -]) increases and approaches or even passes the European Community standard of 50 mg l -1. The objective of this paper is to show the application of statistical process control as a monitoring tool for groundwater pollution from agricultural practices. We propose the autoregressive integrated moving average (ARIMA) model as a management tool to monitoring and reduction of the intrusion of nitrate into the groundwater. This tool should help in setting up useful guidelines for evaluating actual environmental performance against the firm's environmental objectives and targets and regulatory requirements. We concluded that the statistical process control method may be a potentially important way of monitoring groundwater quality that also permits rapid response to serious increases in pollutants concentrations. In doing so, the paper fills an important gap in the water pollution standards and emerging polices (Water Framework directives). © 2010 Springer-Verlag.