Forecasting the seepage loss for lined and un-lined canals using artificial neural network and gene expression programming

Abstract

Canal lining is customarily used to raise water-use effectiveness and reduce seepage loss. The major water losses in an irrigation channel are due to leakage and evaporation. The Egyptian General Integrated Management for Water Resources and Irrigation introduced a proposal for lining the Al-Hagar canal based on these losses. This study investigates the effect of lining in the Al-Hagar canal on flow characteristics, and compares the canal before and after introducing the lining. Additionally, it discusses the most common type of water loss, namely, losses due to seepage. Fieldwork was conducted on the Al-Hagar canal, Al-Saff Center, South of Helwan city, Egypt. The result revealed that the discharge of the canal after the lining is approximately 1.362–1.573 times greater than that of the un-lined section. Water losses in the Al-Hagar canal were 38.736% when un-lined but decreased to 29.253% when lined. The conveyance effectiveness in the un-lined canal, which is approximately 61.26%, increased to 70.75% when the entire canal is lined, which means a 9.483% improvement of conveyance. New relations were introduced using Artificial Neural Network and Gene Expression Programming to forecast the seepage loss in the lined and the un-lined canal as a function of Manning’s coefficient, Froude number and hydraulic radius. The consequences were better using the GEP program than using ANN for the lined and the un-lined canals. The value of the determination coefficient was 0.98, Correlation factor was 0.99, and the RMSE was 0.0017 for lined canals and the value of determination coefficient was 1, Correlation factor was 1, and the RMSE was 0.0003 for un-lined canals.