Artificial neural networks as routine for error correction with an application in Singapore regional model

Sun, Yabin; Babovic, Vladan; Chan, Eng Soon

doi:10.1007/s10236-012-0524-x

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…ANNs have been widely applied in modeling of nonlinear hydrological relationships (Minns & Hall, 1996;Abrahart & See, 2007). A routine for error correction of numerical models was developed using ANN by Sun et al (2012). Streamflow estimation has been carried out using ANN by Nourani et al (2009) and Humphrey et al (2016).…”

Section: Machine Learning In Water Resourcesmentioning

confidence: 99%

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Chadalawada

Herath

Babovic

2020

Water Resources Research

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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. Fixed Models Versus Flexible ModelsDesign of conceptual models traditionally begins with a perceptual model derived from insights gained on the basis of fieldwork and experience, proceeding through a mathematical formulation of the hypothesized structure to the numerically robust implementation in a computer code . Conceptual hydrological modeling can be broadly classified into single-and multiple-hypothesis (often referred to as flexible) modeling approaches.Development of models with fixed structure is based on the identification of a general model structure that is physically realistic and applicable to a reasonably wide range of catchments and climatic conditions. Several

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Section: Machine Learning In Water Resourcesmentioning

confidence: 99%

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Chadalawada

Herath

Babovic

2020

Water Resources Research

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“…Machine learning models have shown encouraging performances in a range of water resources applications, such as rainfallrunoff modelling (Minns and Hall, 1996;Khu et al, 2001;Babovic and Keijzer, 2002;Chiang et al, 2004), streamflow forecasting (Nourani et al, 2009;Meshgi et al, 2014Meshgi et al, , 2015Humphrey et al, 2016;Karimi et al, 2016), estimation of missing data (Elshorbagy et al, 2002), error correction (Sun et al, 2012), water quality modelling (Savic and Khu, 2005;Singh et al, 2011;García-Alba et al, 2019), sediment transport modelling (Babovic and Abbott, 1997;Afan et al, 2014;Safari and Mehr, 2018), reservoir management (Giuliani et al, 2015), prediction of climate variables (Dahamsheh and Aksoy, 2013;Ferreira et al, 2019), because of their potential to apprehend the noise complexity, non-linearity, non-stationarity and dynamism of data (Yaseen et al, 2015). Certainly, if we are only interested in better forecasting results then, the machine learning models might be the preferred choice over the conceptual or process-based models due to their better predictive capability.…”

Section: Machine Learning In Water Resourcesmentioning

confidence: 99%

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Herath¹,

Chadalawada²,

Babovic³

2020

Preprint

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Abstract. Despite showing a great success of applications in many commercial fields, machine learning and data science models in general, show a limited use in scientific fields including hydrology. The approach is often criticized for lack of interpretability and physical consistency. This has led to the emergence of new paradigms, such as Theory Guided Data Science (TGDS) and physics informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles, in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on Genetic Programming (GP) namely Machine Learning Rainfall-Runoff Model Induction Toolkit (ML-RR-MI). ML-RR-MI is cable of developing fully-fledged lumped conceptual rainfall-runoff models for a watershed of interest using the building blocks of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX). In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall-runoff models. This effort is motivated by the desire to address the decreasing meaningfulness of lumped models which tend to particularly deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties are significant. Henceforth, our machine learning approach for rainfall-runoff modelling titled Machine Induction Knowledge-Augmented System Hydrologique Asiatique (MIKA-SHA) captures spatial variabilities and automatically induces rainfall-runoff models for the catchment of interest without any subjectivity in model selection. Currently, MIKA-SHA learns models utilizing the model building components of FUSE and SUPERFLEX. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA’s model induction capabilities have been tested on the Red Creek catchment near Vestry, Mississippi, United States. The resulted model architectures through MIKA-SHA are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists.

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“…This network compensates the forecasting result from a deterministic model. Similar approaches are implemented on hydraulic applications [12][13][14][15] and financial applications [16] . While machine learning capability in approximating any nonlinear or complex system is promising, it is a blackbox approach, which lacks the physical meanings of the actual system structure and its parameters, as well as their impacts on the system.…”

Section: Related Work and Contribution Of The Present Studymentioning

confidence: 99%

Model error correction in data assimilation by integrating neural networks

Zhu

Arcucci

et al. 2019

Big Data Min. Anal.

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In this paper, we suggest a new methodology which combines Neural Networks (NN) into Data Assimilation (DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study.

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Artificial neural networks as routine for error correction with an application in Singapore regional model

Cited by 8 publications

References 10 publications

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Model error correction in data assimilation by integrating neural networks

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