Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran

Panahi, Mahdi; Khosravi, Khabat; Ahmad, Sajjad; Panahi, Somayeh; Heddam, Salim; Melesse, Assefa M.; Omidvar, Ebrahim; Lee, Chang-Wook

doi:10.1016/j.ejrh.2021.100825

Cited by 30 publications

(20 citation statements)

References 94 publications

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“…Table 2 lists the geographical coordinates of all sampling stations. For more information, reference is made to Panahi et al (2021), Sepahvand et al (2021) and Sihag et al (2019). For all mentioned stations given in Table 2, the infiltration rate was measured using the double rings.…”

Section: Methodsmentioning

confidence: 99%

Development and uncertainty analysis of infiltration models using PSO and Monte Carlo method

Podeh

Parsaie

Shahinejad

et al. 2022

Irrigation and Drainage

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The present research aims to calibrate infiltration models including the Kostiakov (KM), modified Kostiakov (MKM), Novel (NM), Philip (PM), Horton (HM) and Soil Conservation Service (SCSM) models using the particle swarm optimization (PSO) algorithm. To satisfy this end, the published data related to the double-ring test in the Davood Rashid and Hunam regions located in Lorestan and Ilam provinces (western Iran) were applied. Then, Monte Carlo analysis (MCA) was used to model the uncertainty of the coefficients of the mentioned models. The lowest accuracy in the Hunam region is related to MKM with the statistical indices coefficient of determination (R 2 ) = 0.901 and root mean square error (RMSE) = 0.023, while in the same region, the highest accuracy is related to the NM with the statistical indices R 2 = 0.988 and RMSE = 0.008. For the Davood Rashid region, the lowest accuracy is related to the HM with the statistical indices R 2 = 0.890 and RMSE = 0.038. In this region, the highest accuracy is related to the NM and MKM with the statistical indices R 2 = 0.987 and RMSE = 0.012. To determine the uncertainty of the parameters of the infiltration models, 10,000 datasets were generated based on observed data and the MCA, and then their range at the 95% confidence level was calculated. According to the MCA, among the models used in the study areas, the NM has less uncertainty and the results of this model can be adopted with more reliability.

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Section: Methodsmentioning

confidence: 99%

Development and uncertainty analysis of infiltration models using PSO and Monte Carlo method

Podeh

Parsaie

Shahinejad

et al. 2022

Irrigation and Drainage

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“…In practice, field infiltration testing must be strictly adhered to in that the test should not be carried out during a rain event, and for rainfall that exceeds 12.7 mm/day, the test should be carried out at least 48 hours after the rain event [20]. Observation time had the highest effect on cumulative infiltration, followed by % silt, % clay, % sand, water content, soil density, and infiltration rate [21]. Table 1 describes the classification of infiltration rates in the tropics and subtropics based on the interval of the basic infiltration rate [14].…”

Section: Infiltration Rate and Classification Based On Basic Infiltra...mentioning

confidence: 99%

Comparison of infiltration model performance based on basic infiltration rate for small watersheds in Papua region, Indonesia

Pristianto,

Suhardjono,

Suhartanto

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Research on soil water infiltration in equatorial climates like Indonesia is limited. Despite Papua accounting for 29% of Indonesia’s surface water, there’s a shortage of hydrological data. This study aimed to identify the best infiltration model tailored to the characteristics of basic infiltration rates in Papua’s small watersheds. Observations were made at 95 points across 11 small watersheds in Papua. Based on equatorial basic infiltration rate theories, analysis classified infiltration values against observation time. The performance of four infiltration models — Horton, Philip, Kostiakov, and Green Ampt — were compared. The results indicated the Philip model as superior, with average scores of 0.901 (R), 0.814 (NSE), and 0.424 (RSR), followed by the Horton and Green Ampt models. These three models showed excellent performance. However, the Kostiakov model was found lacking and needs modification. Further research on rapid and very rapid classifications is vital for enhancing infiltration rate predictions in small equatorial watersheds.

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“…Namely, if rain exceeded 12.7 mm/day, they could only continue the research after 48 hours (Atta-Darkwa et al, 2022). Silt content is the most critical parameter in predicting infiltration rate, and other parameters included: time, clay content, water content, sand content, and soil density (Panahi et al, 2021).…”

Section: Infiltration Ratementioning

confidence: 99%

Soil Textures-Based Evaluation of Horton and Philip's Infiltration Models for Equatorial Small Watersheds

Pristianto,

Suhardjono,

Bisri

et al. 2024

J. Ecol. Eng.

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The global database underscores Indonesia's scant 0.47% global contribution, of which 7.7% is from humid tropical climates. However, existing infiltration studies have primarily focused on specific soil textures and limited research points, resulting in a lack of comprehensive data. This knowledge gap is particularly evident in the Papua region of Indonesia, which boasts many small watersheds with abundant water resources but limited hydrologic data, especially regarding infiltration rates. Previous studies indicated that Horton and Philip's models excelled in equatorial regions but were limited in the number of soil textures and watersheds analysed or focused mainly on larger watersheds. Therefore, this study aimed to address this research gap by conducting a comprehensive analysis of the performance of the Horton and Philip's models across different soil textures in small watersheds, using the Hydrologic Soil Group classification as a reference. A performance analysis was conducted to assess Horton and Philip's performance using the Moriasi technique (based on R, RSR, and NSE best values). Field observations were conducted at 95 points in eleven small watersheds in Papua as representatives of equatorial small watersheds globally. Observations were suspended for 48 hours if rainfall occurred; thus, ten months were needed to finish the observation. The results of this study demonstrated that the Horton model performed exceptionally well for six of nine soil textures, whereas the Philip's model showed excellent performance for five out of nine. The obtained research results were compared with similar studies from Ghana, Nigeria, and India, reinforcing the conclusion that globally, the Horton and Philip's model effectively describes infiltration rates in equatorial small watersheds. Further research was recommended in equatorial small watersheds with sand and loamy sand soil textures, two of the nine soil textures that were not covered in the conducted study.

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Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran

Cited by 30 publications

References 94 publications

Development and uncertainty analysis of infiltration models using PSO and Monte Carlo method

Development and uncertainty analysis of infiltration models using PSO and Monte Carlo method

Comparison of infiltration model performance based on basic infiltration rate for small watersheds in Papua region, Indonesia

Soil Textures-Based Evaluation of Horton and Philip's Infiltration Models for Equatorial Small Watersheds

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