International Journal of Water Resources and Environmental Engineering
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Article Number - 414D80C65672

Vol.9(9), pp. 191-200 , September 2017
ISSN: 2141-6613

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Full Length Research Paper

Application of stochastic models in predicting Lake Malawi water levels

Rodgers Makwinja
  • Rodgers Makwinja
  • Department of Physics and Biochemical Sciences, University of Malawi, The Polytechnic, Private Bag 303, Chichiri, Blantyre 3, Malawi.
  • Google Scholar
Titus Phiri
  • Titus Phiri
  • Senga Bay Fisheries Research Unit, P. O. Box 316, Salima, Malawi.
  • Google Scholar
Ishmael B. M. Kosamu
  • Ishmael B. M. Kosamu
  • Department of Physics and Biochemical Sciences, University of Malawi, The Polytechnic, Private Bag 303, Chichiri, Blantyre 3, Malawi.
  • Google Scholar
Chikumbusko C. Kaonga
  • Chikumbusko C. Kaonga
  • Department of Physics and Biochemical Sciences, University of Malawi, The Polytechnic, Private Bag 303, Chichiri, Blantyre 3, Malawi.
  • Google Scholar

 Received: 15 July 2017  Accepted: 03 August 2017  Published: 30 September 2017

Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0

Stochastic models have proven to be practically fundamental in fields such as science, economics, and business, among others. In Malawi, stochastic models have been used in fisheries to forecast fish catches. Nevertheless, forecasting water levels in major lakes and rivers in Malawi has been given little attention despite the availability of ample historical data. Although previous multichannel seismic surveys revealed the presence of low stands (sediment bypass zone) in Lake Malawi indicating that since the beginning of its formation, important water level fluctuations have been occurring, these previous surveys failed to predict and highlight much more clearly the status of these levels in the future. Therefore, the main objective of the study was to fill these research gaps. The study used Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) processes to select the appropriate stochastic model. Based on lowest Normalized Bayesian Information Criterion (NBIC), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), Maximum Absolute Percentage Error (MAXAPE), Maximum Absolute Error (MAXAE), and Mean Absolute Error (MAE) - ARIMA (0,1,1) model is found suitable for forecasting Lake Malawi water levels which shows negative trend up to 2035. The study further predicted that Lake Malawi water levels will decrease from the current average level of 472.97 m to an average of 468.63 m for the next 18 years (up to 2035).

Key words: Forecasting, Lake Malawi, modelling, stochastic, time series, water levels.

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APA Makwinja, R., Phiri, T., Kosamu, I. B. M., & Kaonga, C. C. (2017). Application of stochastic models in predicting Lake Malawi water levels. International Journal of Water Resources and Environmental Engineering, 9(9), 191-200.
Chicago Rodgers Makwinja, Titus Phiri, Ishmael B. M. Kosamu and Chikumbusko C. Kaonga. "Application of stochastic models in predicting Lake Malawi water levels." International Journal of Water Resources and Environmental Engineering 9, no. 9 (2017): 191-200.
MLA Rodgers Makwinja, et al. "Application of stochastic models in predicting Lake Malawi water levels." International Journal of Water Resources and Environmental Engineering 9.9 (2017): 191-200.

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