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Advances in the aquatic sciences
RESEARCH ARTICLE (Open Access)

Forecasting water consumption on transboundary water resources for water resource management using the feed-forward neural network: a case study of the Nile River in Egypt and Kenya

Anne Wambui Mumbi https://orcid.org/0000-0001-8651-9513 A B , Fengting Li https://orcid.org/0000-0003-3467-9827 A B C , Jean Pierre Bavumiragira A B and Fangnon Firmin Fangninou B
+ Author Affiliations
- Author Affiliations

A College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, PR China.

B State Key Laboratory of Pollution Control and Resource Reuse Study, College of Environmental Science and Engineering, Tongji University, Siping Road 1239, Shanghai, 200092, PR China.

C Corresponding author. Email: fengting@tongji.edu.cn

Marine and Freshwater Research 73(3) 292-306 https://doi.org/10.1071/MF21118
Submitted: 26 April 2021  Accepted: 30 September 2021   Published: 10 November 2021

Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND

Abstract

Water resources are an essential component of a country’s natural resource potential. Pressure on these resources is set to increase due to increased water demand, climate change and rainfall variability. This could lead to conflicts between sectoral users, within or between countries, especially among transboundary countries. Interest in transboundary water resources is a priority, especially where issues such as uncertainty regarding the status of transboundary waterbodies and reductions in water volume persist. In this study, we used the feed-forward neural network to forecast water demand along the Nile River in two countries, Egypt and Kenya. Two scenarios were modelled. Input data for the first scenario included preceding records of precipitation, gross domestic product, population and water use in the agricultural sector. The second scenario observed the effects of the growing economy on water resources by doubling the gross domestic product and keeping all other inputs constant. For Kenya, the results projected a steady increase in water demand throughout the next 20 years for both scenarios. However, for Egypt, the observed trend in both scenarios was a decline in water demand, followed by a steady increase. The results underscore the importance of forecasting for easier future planning and management, and to help governing bodies along transboundary water resources develop timely strategies in the future to alleviate future water shortages and poor management of water resources.

Keywords: feed-forward neural network, Nile River, recurrent neural network, transboundary resources, water demand, water resources.


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