Spatially Distributed Assessment of Channel Seepage Using Geophysics and Artificial Intelligence

Shahbaz Khan, Tariq Rana, Dharmasiri Dassanayake, Akhtar Abbas, John Blackwell, Saud Akbar, Hamza Gabriel

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Estimation of channel seepage is an essential task in improving the management of earthen channel systems. The spatial distribution of seepage rates along the channels must be quantified to establish the economic and environmental merit of reducing conveyance losses. In Australia, due to recurring droughts and irrigation induced salinity concerns, there is much pressure to improve the efficiency of existing water resource use. Saving seepage losses from earthen channels has therefore become an important issue for several reasons including the loss of a valuable resource, maintaining channel assets and reducing accessions to groundwater. In this paper spatial distribution of channel seepage was quantified using artificial neural networks (ANNs). The electromagnetic imaging (EM31) data along with hydraulic conductivity, depth and salinity of groundwater were correlated with Idaho seepage meter measurements using the ANNs. It is estimated that over 42 millionm3 of water can be lost annually from 500 km of channel in the Murrumbidgee Irrigation Area. The distributed channel seepage analysis indicates that most significant seepage (>20 mm/day) occurs in less than 32% of the surveyed channel length; therefore it is important to target channel lining investments to the leakiest parts ' ''hotspots'' ' of the channel system.
Original languageEnglish
Pages (from-to)307-320
Number of pages14
JournalIrrigation and Drainage
Volume58
Issue number3
DOIs
Publication statusPublished - Jul 2009

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