Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.eswa.2020.114498 |
Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling (R) | |
Chakraborty, Debaditya; Basagaoglu, Hakan; Winterle, James | |
通讯作者 | Chakraborty, D (corresponding author), Univ Texas San Antonio, Dept Construct Sci, 501 W Cesar E Chavez Blvd, San Antonio, TX 78207 USA. |
来源期刊 | EXPERT SYSTEMS WITH APPLICATIONS |
ISSN | 0957-4174 |
EISSN | 1873-6793 |
出版年 | 2021 |
卷号 | 170 |
英文摘要 | Due to their enhanced predictive capabilities, noninterpretable machine learning (ML) models (e.g. deep learning) have recently gained a growing interest in analyzing and modeling earth & planetary science data. However, noninterpretable ML models are often treated as ?black boxes? by end-users, which could limit their applicability in critical decision making processes. In this paper, we compared the predictive capabilities of three interpretable ML models with three noninterpretable ML models to answer the overarching question: Is it essential to use noninterpretable ML models for enhanced model predictions from hydro-climatological datasets? The ML model development and comparative analysis were performed using measured climate data and synthetic reference crop evapotranspiration (ETo) data, with varying levels of missing values, from five weather stations across the karstic Edwards aquifer region in semi-arid south-central Texas. Our analysis revealed that interpretable tree based ensemble models produce comparable results to noninterpretable deep learning models on structured hydro-climatological datasets. We showed that the tree-based ensemble model is also capable of imputing varying levels of missing climate data at the weather stations, employing the newly developed sequential transfer-learning technique. We applied an explainable machine learning (eXML) framework to quantify the global order of importance of hydro-climatic (predictor) variables on ETo, while highlighting the local dependencies and interactions amongst the predictors and ETo. The eXML framework also revealed the inflection points of the climate variables at which the transition from low to high daily ETo rates occur. The ancillary explainability of ML models are expected to increase users? confidence and support any future decision-making process in water resource management. |
英文关键词 | Deep learning Boosting Transfer learning Hydroclimate Reference crop evapotranspiration Model explainability |
类型 | Article |
语种 | 英语 |
收录类别 | SCI-E |
WOS记录号 | WOS:000626414500001 |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science |
WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/368772 |
作者单位 | [Chakraborty, Debaditya] Univ Texas San Antonio, Dept Construct Sci, 501 W Cesar E Chavez Blvd, San Antonio, TX 78207 USA; [Basagaoglu, Hakan; Winterle, James] Edwards Aquifer Author, 900 E Quincy St, San Antonio, TX 78215 USA |
推荐引用方式 GB/T 7714 | Chakraborty, Debaditya,Basagaoglu, Hakan,Winterle, James. Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling (R)[J],2021,170. |
APA | Chakraborty, Debaditya,Basagaoglu, Hakan,&Winterle, James.(2021).Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling (R).EXPERT SYSTEMS WITH APPLICATIONS,170. |
MLA | Chakraborty, Debaditya,et al."Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling (R)".EXPERT SYSTEMS WITH APPLICATIONS 170(2021). |
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