International Journal of
Water Resources and Environmental Engineering

  • Abbreviation: Int. J. Water Res. Environ. Eng.
  • Language: English
  • ISSN: 2141-6613
  • DOI: 10.5897/IJWREE
  • Start Year: 2009
  • Published Articles: 311

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


Alonso A, Garcia-Martos C (2012). Time series and stochastic processes. Madrid: Universidad Carlos III de Madrid. Available online at 



Aksoy H, Unal N, Eris E, Yuce M (2013). Stochastic modeling of Lake Van water level time series with jumps and multiple trends. Hydrol. Earth Syst.Sci. 17:2297-2303.


Box G, Jenkins G (1970). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Da. 


Box G, Jenkins G, Reinsel G, Ljung G (2015). Time Series Analysis: Forecasting and Control. Hoboken, NJ, USA: John Wiley and Sons.


Cao L, Francis E (2003). Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Trans. Neural Networks 14(6):1506-1518.


Chang X, Gao M, Wang Y, Hou X (2012). Seasonal Autoregressive Integrated Moving Average Model For Precipitation Time Series. J. Maths. Stat. 8(4):500-505.


Chatfield C (1996). Model uncertainty and forecast accuracy. J. Forecasting pp. 495-508.


Chung M (2009). Lecture on time series diagnostic test. Taipei 115, Taiwan: Institute of Economics, Academia Sinica.  


Cochrane H (1997). Time Series for Macroeconomics and Finance. Chicago: Graduate School of Business, University of Chicago, Spring. Available online at 



Cryer J, Chan K. (2008). Time Series Analysis with Application in R 2nd ED. New Yolk: Springer.


Czerwinski I, Guti'errez-Estrada J, Hernando-Casal J (2007). Short-term forecasting of halibut CPUE: Linear and non-linear univariate approaches. Fisheries Res. 86:120-128.


De Vas A (1994). Pliocene en Kwartaire evolutie van het Livingstone bekken (Malawi rift, Tanzanie) afgeleid uithoge-resolutie reflectieseismische profielen.Licenciaat theis. Belgium: s, Universiteit Gent. 


Dixey F (1924). Lake level in relation to rainfall and sunspots. Nature 114:659-661.


Drayton R (1979). A study of the causes of the abnormally high levels of Lake Malawi. Lilongwe: Wat. Resour. Div. Tech. Pap.No 5.


Drayton R (1984). Variation in the level of Lake Malawi. J.Hydrol.Sci. 29:1-12.


GoM (2005). National Spatial Data Center, Ministry Lands Housing physical Planning and Surveys, Lilongwe, Malawi. 


Guti'errez-Estradade J, L'opez-Luque E, Pulido-Calvo I (2004). Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla Anguilla L.) Intensive rearing system. Aquat. Eng. 31:183-203.


Hipel K, McLeod A (1994). Time Series Modelling of Water Resources and Environmental Systems. Amsterdam: Elsevier 1994.


IBM Corp (2011). IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp. Available online at 



Johnson T, Davis T (1989). High resolution profiles from Lake Malawi, Africa. J. Afr. Earth. Sci. 8:383-392.


Kantz H, Schreiber T (2004). Nonlinear Time Series Analysis 2nd Edn. Cambridge: Cambridge University Press.


Kaunda P (2015). Investigating the Impacts of Cliamte Change on the Levels of lake Malwi Thesis, Department of meteology, university of nairobi, Kenya.


Kidd C (1983). A water resources evaluation of Lake Malawi and the Shire River. Geneva:UNDP project MLW/77/012, World Meteological Organisation.


Kumambala P, Ervine A (2010). Water Balance Model of Lake Malawi and its Sensitivity to Climate Change. Open. Hydrol. J. 4:152-162.


Kumambala P (2010). Sustainability of water resources development for Malawi with particular emphasis on North and Central Malawi. PhD thesis. Glasgrow, UK: University of Glasgrow. 


Lazaro M, Jere W (2013). The Status of the Commercial Chambo (Oreochromis Species) fishery in Malaŵi: A Time Series Approach. Intl. J. Sci. Technol. 3:1-6.


Lombardo R, Flaherty J (2000). Modelling Private New Housing Starts In Australia. Pacific-Rim Real Estate Society Conference Sydney: University of Technology Sydney (UTS) pp. 24-27.


Ljung G, Box G (1978). On a measure of lack of fit in time series models. J. Bio. 65:297-303.


Mulumpwa M, Jere W, Mtethiwa A, Kakota T, Kang'ombe J (2016). Application Of Forecasting In Determining Efficiency Of Fisheries Management Strategies Of Artisanal Labeo mesops Fishery Of Lake Malawi. Int. J. sci. Tech. Resea 5(09):28-40.


Neuland H (1984). Abnormal high water levels of Lake Malawi? -An attempt to assess the future behaviour of the lake water levels. Geo. J. 9:323-334.


Sankar I (2011). Stochastic Modelling Approach for forecasting fish product export in Tamilnadu. J. R. Resea. Sci.Technol. 3(7):104-108.


Scholz E, Rozendahl B (1988). Low lake stands in Lakes Malawi and Tanganyika, East Africa, delineated from multifold seismic data. J. Sci. 240:1645-1648.


Shela O (2000). Naturalisation of Lake Malawi Levels and Shire River Flows: Challenges of Water Resources Research and Sustainable Utilisation of the Lake Malawi-Shire River System. Sustainable Use of Water Resources Maputo: 1st WARFSA/WaterNet Symposium, pp. 1-12.


Singini W, Kaunda E, Kasulo V, Jere W (2012). Modelling and Forecasting Small Haplochromine Species (Kambuzi) Production in Malaŵi-A Stochastic Model Approach. Intl. J. Sci. Technol. Resea 1:69-73.


Stuffer D, Dhumway R (2010). Time series Analysis and its Application 3rd Ed. New Yolk: Springer.


Yevjevich V (1972). Probability and Statistics in Hydrology. Colorado: Water Resources Pub. Available online at 



Zhang G (2007). A neural network ensemble method with jittered training data for time series forecasting. J. Infor. Sci. 177:5329-5346.


Zhang G (2003). Time series forecasting using a hybrid ARIMA and neural network model. J. Neuroc. 50:159-175.


Zindi I, Singini W, Mzengeleza K (2016). Forecasting Copadichromis (Utaka) Production for Lake Malaŵi, Nkhatabay Fishery- A Stochastic Model Approach. J. Fish. Livest. Pro