Characterization and control of irrigation canal seepage losses: A review and perspective focused on field data

Lund, A.A. Rehman; Gates, Timothy K.; Scalia, Joseph

doi:10.1016/j.agwat.2023.108516

Cited by 7 publications

(2 citation statements)

References 123 publications

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“…Review synthesized and evaluated by (Lund et al, 2023) indicated that seepage from unlined canals uctuates from 0.1 to 1 m 3 /day per m 2 . It was affected by factors such as properties of canal bed soil, geometry, wetted perimeter, ow depth, velocity, level of groundwater, vegetation, and water properties.…”

Section: Introductionmentioning

confidence: 99%

The effect of lining hydraulic properties on the efficiency and cost of irrigation canal reconstruction

Zelenakova,

Zeleňáková,

Arifjanov

et al. 2023

Preprint

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Irrigation canals play an important role in the economic development of Uzbekistan which is located in arid zone and shared user of transboundary water resources. Almost three fourth of the irrigation network in Uzbekistan is unlined that subject to some natural processes such as vegetation, erosion, sedimentation, and seepage. The efficiency of the irrigation networks in Uzbekistan is about 63%. Lining of canals is the major protection measure against water scarcity for Uzbekistan. This study aims to investigate how the hydraulic properties of different lining materials such as geomembrane, concrete, asphalt, sand mixed bentonite and stone pitching affect the rehabilitation cost of canals. In this study, field data and hydrodynamic model HEC-RAC are used for analysis of using different lining materials on the efficiency of the canal and then the costs of rehabilitation were compared. To apply the hydrodynamic model HEC-RAS, flow and bathymetric parameters were measured by employing the acoustic Doppler current profiler system for a reach of length 1.0 km in Tashkent magistral canal. The obtained R2 between modeled and measured data equals to 0.86. The use of different lining materials such as geomembrane, concrete, asphalt, sand mixed bentonite and stone pitching affect the channel geometry with different ranges from 21.69 to 34.41 per running meter (RMT). With different cross-sectional values, rehabilitation of the first reach of Toshkent magistral canal costs about $ 260, 688, 536, 286, 210 thousand respectively. In the point of lifetime, geomembrane then concrete, and stone pitching showed the minimum annual coverage value containing $32, 46, 10 thousand per year respectively. Stone pitching with the longest durability is the most efficient technique to improve water-use efficiency of Toshkent magistral canal. This study could help the policy makers to select the best material for lining based on the lifetime, cost and durability.

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Section: Introductionmentioning

confidence: 99%

The effect of lining hydraulic properties on the efficiency and cost of irrigation canal reconstruction

Zelenakova,

Zeleňáková,

Arifjanov

et al. 2023

Preprint

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“…To effectively estimate seepage loss using numerical models, selecting consistent boundary conditions and developing seepage models based on these conditions is crucial. Previous research has explored seepage from earthen canals through direct measurements (Lund et al, 2023) or analytical studies (Sharma and Chawla, 1979;Chahar, 2007;Osman and Rahman, 2008;Ghazaw, 2011;Carabineanu, 2012;Uchdadiya and Patel, 2014). Empirical equations have been used and compared in earlier studies (Mowafy, 2001;Saha, 2015), while more recent research increasingly uses numerical modeling techniques (Salmasi and Abraham, 2020;El-Molla and El-Molla, 2021a,b;El-Molla and Eltarabily, 2023;Eltarabily et al, 2023a,b).…”

Section: Introductionmentioning

confidence: 99%

Predicting seepage losses from lined irrigation canals using machine learning models

Eltarabily,

Abd-Elhamid,

Zeleňáková

et al. 2023

Front. Water

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IntroductionEfficient water resource management in irrigation systems relies on the accurate estimation of seepage loss from lined canals. This study utilized machine learning (ML) algorithms to tackle this challenge in seepage loss prediction.MethodsFirstly, seepage flow through irrigation canals was modeled numerically and experimentally using Slide2 and physical models, respectively. Then, the Slide2 model results were compared to the experimental tests. Thus, the model was used to conduct 600 simulation scenarios. A parametric analysis was performed to investigate the effect of canal geometry and liner properties on seepage loss. Based on the conducted scenarios, ML models were developed and evaluated to determine the best predictive model. The ML models included non-ensemble (regression-based, evolutionary, neural network) and ensemble models. Non-ensemble models (adaptive boosting, random forest, gradient boosting). There were four input ratios in these models: bed width to water depth, side slope, liner to soil hydraulic conductivity, and liner thickness to water depth. The output variable was the seepage loss ratio. Seven performance indices and k-fold cross-validation were employed to evaluate reliability and accuracy. Moreover, a sensitivity analysis was conducted to investigate the significance of each input in predicting seepage loss.Results and discussionThe findings revealed that the Artificial Neural Network (ANN) model was the most dependable predictor, achieving the highest determination-coefficient (R2) value of 0.997 and root-mean-square-error (RMSE) of 0.201. The eXtreme Gradient Boosting (XGBoost) followed the ANN model closely, which achieved an R2 of 0.996 and RMSE of 0.246. Sensitivity analysis showed that liner hydraulic conductivity is the most significant parameter, contributing 62% predictive importance, while the side slope has the lowest significance. In conclusion, this study presented efficient and cost-effective models for predicting seepage loss, eliminating the need for resource-intensive experimental or field investigations.

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Determining Seepage Loss Predictions in Lined Canals Through Optimizing Advanced Gradient Boosting Techniques

Elshaarawy,

Elmasry,

Selim

et al. 2024

Water Conserv Sci Eng

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Characterization and control of irrigation canal seepage losses: A review and perspective focused on field data

Cited by 7 publications

References 123 publications

The effect of lining hydraulic properties on the efficiency and cost of irrigation canal reconstruction

The effect of lining hydraulic properties on the efficiency and cost of irrigation canal reconstruction

Predicting seepage losses from lined irrigation canals using machine learning models

Determining Seepage Loss Predictions in Lined Canals Through Optimizing Advanced Gradient Boosting Techniques

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