Trend Analysis and Forecasting of Water Level in Mtera Dam Using Exponential Smoothing

Автор: Filimon Abel Mgandu, Mashaka Mkandawile, Mohamed Rashid

Журнал: International Journal of Mathematical Sciences and Computing @ijmsc

Статья в выпуске: 4 vol.6, 2020 года.

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This study presents trend analysis and forecasting of water level in Mtera dam. Data for water level were obtained from Rufiji Basin Development Authority (RUBADA). The study analyzed trend of water level using time series regression while forecasting of water level in Mtera dam was done using Exponential smoothing. Results revealed that both maximum and minimum water level trends were decreasing. Forecasted values show that daily water level will be below 690 (m.a.s.l) which is the minimum level required for electricity generation on 2023. It was recommended that proper strategies should be taken by responsible authorities to reduce effects that may arise. Strategies my include constructing small dams on upper side of Mtera dam to harvest rain water during rainy season as reserves to be used on dry season. In long run Tanzania Electric Supply Company (TANESCO) should invest into alternative sources of energy.

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Trend Analysis, Time series, Forecasting, Exponential smoothing, Mtera dam

Короткий адрес: https://sciup.org/15017554

IDR: 15017554   |   DOI: 10.5815/ijmsc.2020.04.03

Список литературы Trend Analysis and Forecasting of Water Level in Mtera Dam Using Exponential Smoothing

  • Abbaspour KC, Faramarzi M, Ghasemi SS and Yang H. Assessing the impact of climate change on water resources in Iran. Water resources research. 2009; 45:10.
  • Adhikari, R. and Agrawal, RK. An introductory study on time series modeling and forecasting. arXiv preprint arXiv: 2013; 1302.6613.
  • Altunkaynak, A. Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water resources management. 2007; 21(2):399-408.
  • Ashton PJ. Avoiding conflicts over Africa's water resources. AMBIO: A Journal of the Human Environment. 2002;31(3): 236-242.
  • Bates BC, Kundzewicz ZW, Wu S and Palutikof JP. Climate change and water. Technical paper of the intergovernmental panel on climate change, IPCC secretariat, Geneva. Climate Change Policy with a Renewed Environmental Ethic. 2008; 21:85-101.
  • Bodansky D. The United Nations framework convention on climate change: a commentary.Yale J. Int'l l. 1993;18:451.
  • Hyndman RJ and Athanasopoulos G. Forecasting: principles and practice. 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. Accessed on May 2019. 2018
  • IPCC. Report of the nineteenth session of the intergovernmental panel on climate change (IPCC) Geneva, 17-20 (am only) April 2002. 2007.
  • Mdemu MV and Magayane MD. Conflict of water use between hydropower and irrigation in Tanzania: The conundrum of sectoral policy approaches to water resources development. 2005.
  • Omambia CS and Gu. The cost of climate change in Tanzania: impacts and adaptations. Journal of American Science. 2010;6(3).
  • Ondimu, S. and Murase, H. Reservoir level forecasting using neural networks: Lake Naivasha. Biosystems engineering. 2007: 96(1):135-138.
  • Orindi VA and Murray LA. Adapting to climate change in East Africa: a strategic approach (No. 117). International Institute for Environment and Development. 2005.
  • Raicharoen T, Lursinsap C and Sanguanbhokai P. Application of critical support vector machine to time series prediction. In Proceedings of the 2003 International Symposium on Circuits and Systems. 2003;5.
  • Snipes, M. and Taylor, D.C. Model selection and Akaike Information Criteria: An example from wine ratings and prices. Wine Economics and Policy. 2014; 3(1):3-9.
  • Sreekanth, P.D., Geethanjali, N., Sreedevi, P.D., Ahmed, S., Kumar, N.R. and Jayanthi, P.K. Forecasting groundwater level using artificial neural networks. Current science. 2009; 933-939.
  • United Republic of Tanzania (URT). National adaptation programme of action (NAPA). Vice president's office, division of environment. Government printers, Dar es Salaam. 2007.
  • UN-WATER. Water a shared responsibility. The United Nations, World Water Development Report 2 (601). UN-WATER/WWAP/2006/3. 2006.
  • Water words dictionary. Division of Water Planning. 1998.
  • Yanda PZ, Kangalawe RY and Sigalla RJ. Climatic and socio-economic influences on malaria and cholera risks in the Lake Victoria region of Tanzania. 2005.
  • Zohary T and Ostrovsky I. Ecological impacts of excessive water level fluctuations in stratified freshwater lakes. Inland Waters. 2011;1(1):47-59.
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