Abstract
Abstract. A comprehensive data driven modeling experiment is presented in a two-part paper. In this first part, an extensive data-driven modeling experiment is proposed. The most important concerns regarding the way data driven modeling (DDM) techniques and data were handled, compared, and evaluated, and the basis on which findings and conclusions were drawn are discussed. A concise review of key articles that presented comparisons among various DDM techniques is presented. Six DDM techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and K-nearest neighbors are proposed and explained. Multiple linear regression and naïve models are also suggested as baseline for comparison with the various techniques. Five datasets from Canada and Europe representing evapotranspiration, upper and lower layer soil moisture content, and rainfall-runoff process are described and proposed, in the second paper, for the modeling experiment. Twelve different realizations (groups) from each dataset are created by a procedure involving random sampling. Each group contains three subsets; training, cross-validation, and testing. Each modeling technique is proposed to be applied to each of the 12 groups of each dataset. This way, both prediction accuracy and uncertainty of the modeling techniques can be evaluated. The description of the datasets, the implementation of the modeling techniques, results and analysis, and the findings of the modeling experiment are deferred to the second part of this paper.
Highlights
Data driven modeling (DDM) techniques have been in use for nearly two decades for hydrological modeling, prediction, and forecasting
The findings indicated that genetic programming (GP)-derived streamflow forecasting models were generally favored for forecasting over artificial neural networks (ANNs)
In order to achieve the objectives of this paper with regard to the comparative predictive performance of various DDM techniques, first, a set of distinctive modeling techniques were identified
Summary
Data driven modeling (DDM) techniques have been in use for nearly two decades for hydrological modeling, prediction, and forecasting. Cherkassky et al (2006) have listed the quality of the datasets, choosing robust learning methods that can handle heterogeneous data, and the need for uncertainty estimates associated with predictions as some of the main issues and challenges facing computational intelligence in earth sciences. Abrahart et al (2008) have used the example of neural network applications to highlight the shortcomings of the present approach, and how to build stronger foundations. Their argument can be generalized to apply to other data driven techniques.
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