Data-driven modelling: some past experiences and new approaches

By Dimitri P. Solomatine, Avi Ostfeld. 2008

644 citations recorded by the source corpus.

Subfields: Environmental Engineering, Statistical and Nonlinear Physics, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/1de20dfc13358f18dfd16618b1d158b2c37ec7f1

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In Environmental Engineering

In Statistical and Nonlinear Physics

In Water Science and Technology

Citations in the corpus

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “[20] Artificial neural networks, genetic programming, evolutionary regression, fuzzy-rule based systems, support vector machines, chaos theory, instance-based learning, regression trees River flow forecasting, river basin flow prediction, contaminant transport Testing, human expertise, uncertainty e”
Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction (2016, methodology): “For data preprocessing, wavelet transform (WT) has been found to be a good choice in recent years (He et al. 2014; Suryanarayana et al. 2014; Feng et al. 2015).”
Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques (2010, methodology): “Their accurate modeling by building of a single global model is sometimes not 71 possible (Solomatine and Ostfeld, 2008).”
Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques (2009, methodology): “Modular models were therefore proposed where subprocesses are first of all identified and then separate models (also called local or expert model) are established for each of them [Solomatine and Ostfeld, 2008].”
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, background): “Streamflow prediction models are generally classified in two broad categories (Solomatine and Ostfeld 2008); (i) physically-based model, and (ii) data-driven model.”
Prediction of rainfall time series using modular soft computingmethods (2013, background): “Modular models are therefore proposed where sub-processes are first of all identified and then separate models (also called local or expert model) are established for each of them (Solomatine and Ostfeld, 2008).”
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada (2012, background): “Two very recent publications [ Solomatine and Ostfeld , 2008; Maier et al. , 2010] on the current status and future directions of the use of data-based modeling in the area of water resources variable forecasting highlighted several critical issues that need to be explored in greater detail,…”
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, background): “The reason for this is that previous reviews have generally focused on river systems (Solomatine and Ostfeld, 2008; Maier et al., 2010), at the expense of other aspects of water resources systems.”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “…(see Table 2) concerning particular sub-sets of AI models used in hydrology or specifically on hydrological modeling have explored this topic (Abrahart et al., 2012; ASCE, 2000; Dawson and Wilby, 2001; Kalteh et al., 2008; Maier and Dandy, 2000; Maier et al., 2010; Solomatine and Ostfeld, 2008).”