Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR028862 |
Towards Developing a Generalizable Pedo-Transfer Function for Saturated Hydraulic Conductivity using Transfer Learning and Predictor Selector Algorithm | |
Suraj Jena; Binayak P. Mohanty; Rabindra Kumar Panda; Meenu Ramadas | |
2021-06-26 | |
发表期刊 | Water Resources Research
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出版年 | 2021 |
英文摘要 | Pedotransfer functions (PTFs) are being instrumental in saturated hydraulic conductivity (Ks) estimation. Despite various advancements, the performance of existing generic Ks predicting PTFs need augmentation. This study developed a robust Ks predicting PTF using a machine learning (ML) algorithm and exhaustive dataset for 324 soils with 28 properties sampled over a tropical savanna region of India. Four ML algorithms were evaluated for this purpose, and random forest (RF) outperformed all others. A substantial improvement to the prediction by RF-based PTF was achieved through predictor selection using a hybrid wrapper-embedded algorithm. The predictor selection algorithm selected eight pertinent predictors (HID-S): S, Si, C, FSF, Cu, GMD, D60, and D10. The mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) obtained as average ten-fold cross-validation scores for RF algorithm training with HID-S were 0.87, 1.47, 0.94, and 0.94, respectively. The developed PTF (RF-HID-S) was evaluated alongside the recently published PTFs by Araya & Ghezzehei (2019), within and outside the study region. In that process, it was observed that the RF-HID-S possessed superior prediction proficiency compared to the recently published and commonly used PTFs in both cases. These findings mark RF-HID-S as the most robust generalizable PTF, which may further be evaluated in different parts of the world. Moreover, looking at the performance of the eight selected predictors within and outside the study region, they can be considered for experiment design globally to make Ks estimation accurate and cost-effective. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/333672 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Suraj Jena,Binayak P. Mohanty,Rabindra Kumar Panda,et al. Towards Developing a Generalizable Pedo-Transfer Function for Saturated Hydraulic Conductivity using Transfer Learning and Predictor Selector Algorithm[J]. Water Resources Research,2021. |
APA | Suraj Jena,Binayak P. Mohanty,Rabindra Kumar Panda,&Meenu Ramadas.(2021).Towards Developing a Generalizable Pedo-Transfer Function for Saturated Hydraulic Conductivity using Transfer Learning and Predictor Selector Algorithm.Water Resources Research. |
MLA | Suraj Jena,et al."Towards Developing a Generalizable Pedo-Transfer Function for Saturated Hydraulic Conductivity using Transfer Learning and Predictor Selector Algorithm".Water Resources Research (2021). |
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