Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026933 |
Hydrologically Informed Machine Learning for Rainfall-Runoff Modeling: A Genetic Programming-Based Toolkit for Automatic Model Induction | |
Chadalawada, Jayashree; Herath, H. M. V. V.; Babovic, Vladan | |
2020-04-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | Singapore |
英文摘要 | Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State-of-art GP applications in rainfall-runoff modeling so far used the algorithm as a short-term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall-runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. Key Points This paper presents a novel machine learning algorithm, which is guided through the incorporation of existing hydrological knowledge Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming In the present case, the building blocks of flexible hydrological modeling frameworks represent elements of the background knowledge |
英文关键词 | rainfall-runoff modeling flexible conceptual modeling framework machine learning genetic programming |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000538987800013 |
WOS关键词 | ARTIFICIAL NEURAL-NETWORKS ; THEORY-GUIDED DATA ; DATA-DRIVEN ; STREAMFLOW ; CAPABILITIES ; SIMULATION ; PREDICTION ; SCIENCE ; ERROR |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
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/280634 |
专题 | 资源环境科学 |
作者单位 | Natl Univ Singapore, Dept Civil & Environm Engn, Singapore, Singapore |
推荐引用方式 GB/T 7714 | Chadalawada, Jayashree,Herath, H. M. V. V.,Babovic, Vladan. Hydrologically Informed Machine Learning for Rainfall-Runoff Modeling: A Genetic Programming-Based Toolkit for Automatic Model Induction[J]. WATER RESOURCES RESEARCH,2020,56(4). |
APA | Chadalawada, Jayashree,Herath, H. M. V. V.,&Babovic, Vladan.(2020).Hydrologically Informed Machine Learning for Rainfall-Runoff Modeling: A Genetic Programming-Based Toolkit for Automatic Model Induction.WATER RESOURCES RESEARCH,56(4). |
MLA | Chadalawada, Jayashree,et al."Hydrologically Informed Machine Learning for Rainfall-Runoff Modeling: A Genetic Programming-Based Toolkit for Automatic Model Induction".WATER RESOURCES RESEARCH 56.4(2020). |
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