Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Rodgers Makwinja, Seyoum Mengistou, Emmanuel Kaunda, Tena Alemiew, Titus Bandulo Phiri, Ishmael Bobby Mphangwe Kosamu and Chikumbusko Chiziwa Kaonga
Additional contact information
Rodgers Makwinja: African Centre of Excellence for Water Management, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia
Seyoum Mengistou: African Centre of Excellence for Water Management, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia
Emmanuel Kaunda: African Centre of Excellence in Aquaculture and Fisheries (AquaFish), Lilongwe University of Agriculture and Natural Resources, P.O. Box 219, Lilongwe, Malawi
Tena Alemiew: Water and Land Resource Centre of Addis Ababa University, P.O. Box 3880, Addis Ababa, Ethiopia
Titus Bandulo Phiri: Senga Bay Fisheries Research Centre, P.O. Box 316, Salima, Malawi
Ishmael Bobby Mphangwe Kosamu: Physics and Biochemical Sciences Department, The Malawi Polytechnic, University of Malawi, P/Bag 303, P.O. Box 537, Chichiri, Blantyre, Malawi
Chikumbusko Chiziwa Kaonga: Physics and Biochemical Sciences Department, The Malawi Polytechnic, University of Malawi, P/Bag 303, P.O. Box 537, Chichiri, Blantyre, Malawi

Forecasting, 2021, vol. 3, issue 1, 1-17

Abstract: Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E 2 ), coefficient of determination (R 2 ), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R 2 , and E 2 . The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

Keywords: ARIMA models; CPUE; fish landings; forecasting; lake Malombe; time series approach (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-9394/3/1/4/pdf (application/pdf)
https://www.mdpi.com/2571-9394/3/1/4/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:3:y:2021:i:1:p:4-55:d:495717

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().