Genetic Programming Technique Applied for Flash-Flood Modelling Using Radar Rainfall Estimates

Abstract

Abstract:The rainfall-runoff transformation is a highly complex dynamic process and the development of fast and robust modelling instruments has always been one of the most important topics for hydrology. Over time, a significant number of hydrological models have been developed with a clear trend towards a process-based approach. The downside of these types of models is the significant amount of data required for building the model and for the calibration process: in practice, the collection of all necessary data for such models proves to be a difficult task. In order to cope with this issue, various data-driven modelling techniques have been introduced for hydrological modelling as an alternative to more traditional approaches, on the basis of their capacity of mapping out complex relationships from observation data. Having the capacity to generate meaningful mathematical structures as results, genetic programming (GP) presents a high potential for rainfall-runoff modelling as a data-driven method. Using ground and radar rainfall observation, the aim of this study is to investigate the GP technique capability for modelling the rainfall-runoff process, taking into consideration a flash-flood event.

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

“…Besides the use of support vector machines, the authors highlighted the use of a variety of Artificial Neural Networks (ANN) derived models, such as neuro-fuzzy, adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and multilayer perceptron (MLP), as the more frequent models in the literature. More sophisticated ML-based models such as genetic programming [182] have also been explored with satisfactory results. Nonetheless, other ML-based models as those based on decision trees (DT) are less complex algorithms that have just recently been explored by using radar rainfall [183].…”

Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model

“…Some authors [2,4,5] provide reviews of the application of machine learning-based models for this purpose. Several streamflow forecasting studies [23][24][25][26][27] use data-driven models employing radar-derived rainfall as input; this requires a preliminary step for transforming reflectivity (native ground radar variable) into rainfall rate [14]. Either way, derivation of radar rainfall estimates remains an intensive task that needs to be tackled before using ground radar data for discharge forecasting.…”

A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

“…To the best of our knowledge, after a broader literature review, and based on surveys such as [9,[17][18][19], just three papers were published using machine learning techniques on sub-hourly rainfall and hydrological data: [20] in Austria, [21] in Brazil, and [22] in Romania. The first two papers proposed a neural network and the third a genetic programmingbased hydrological model using rain gauge and weather radar data for a small and steep basin.However, none of them analyzed the model's performance considering different accumulated rainfall in the input layer.…”