.png){kind=icon}
返回
Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling ®
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114498.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Deep learning
Boosting
Transfer learning
Hydroclimate
Reference crop evapotranspiration
Model explainability
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China基于混合极限学习机模型和生物优化算法的日参考蒸散量预测: 在中国对比气候中的应用.png)
JOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS
PREAI
分享收藏Improved Particle Size Control for the Dispersion Polymerization of Methyl methacrylate in Supercritical Carbon Dioxide超临界二氧化碳中甲基丙烯酸甲酯分散聚合的改进粒度控制
OAAI
OAAI分享收藏Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead停止解释高风险决策的黑盒机器学习模型，而改用可解释的模型OAAI
OAAI分享收藏
查看更多内容