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

2017 ◽  
Vol 13 (4) ◽  
pp. 27-38 ◽  
Author(s):  
Cristian Dinu ◽  
Radu Drobot ◽  
Claudiu Pricop ◽  
Tudor Viorel Blidaru
Keyword(s):  
Genetic Programming ◽  
Flash Flood ◽  
Data Driven ◽  
Observation Data ◽  
Rainfall Runoff ◽  
Clear Trend ◽  
Programming Technique ◽  
Radar Rainfall ◽  
Complex Relationships ◽  
Traditional Approaches

AbstractThe 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.

scholarly journals Rainfall Runoff Modelling Based on Genetic Programming

2002 ◽  
Vol 33 (5) ◽  
pp. 331-346 ◽  
Author(s):  
Vladan Babovic ◽  
Maarten Keijzer
Keyword(s):  
Genetic Programming ◽  
Conceptual Models ◽  
Linear Time ◽  
Physical Models ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Considerable Time ◽  
Hydrologic Models ◽  
Spatially Distributed ◽  
Using Data

The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).

scholarly journals FLASH-FLOOD MODELLING WITH ARTIFICIAL NEURAL NETWORKS USING RADAR RAINFALL ESTIMATES

2017 ◽  
Vol 13 (3) ◽  
pp. 10-20 ◽  
Author(s):  
Cristian Dinu ◽  
Radu Drobot ◽  
Claudiu Pricop ◽  
Tudor Viorel Blidaru
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flash Flood ◽  
Good Time ◽  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Space Resolution ◽  
Ann Models ◽  
Artificial Neural ◽  
Gauging Station

Abstract The use of artificial neural networks (ANNs) in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.

scholarly journals SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 872
Author(s):  
Vesna Đukić ◽  
Ranka Erić
Keyword(s):  
Soil Moisture ◽  
Czech Republic ◽  
Hydrological Model ◽  
Flash Flood ◽  
Model Performance ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
River Catchment ◽  
Hydrological Variable ◽  
Model Structures

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

An application of the genetic programming technique to strategy development

2009 ◽  
Vol 36 (3) ◽  
pp. 5157-5161 ◽  
Author(s):  
Koun-Tem Sun ◽  
Yi-Chun Lin ◽  
Cheng-Yen Wu ◽  
Yueh-Min Huang
Keyword(s):  
Genetic Programming ◽  
Strategy Development ◽  
Programming Technique

Flash Flood Prediction on Shoal Creek: or Why Radar Rainfall?

2010 ◽  
Vol 2010 (6) ◽  
pp. 193-200
Author(s):  
Baxter Vieux ◽  
Jean Vieux ◽  
Susan Janek ◽  
Janna Renfro
Keyword(s):  
Flash Flood ◽  
Radar Rainfall ◽  
Flood Prediction

scholarly journals On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment

2019 ◽  
Author(s):  
Maxime Jay-Allemand ◽  
Pierre Javelle ◽  
Igor Gejadze ◽  
Patrick Arnaud ◽  
Pierre-Olivier Malaterre ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Weather Forecasting ◽  
Flash Flood ◽  
Estimation Method ◽  
Distributed Hydrological Model ◽  
Initial State ◽  
Ungauged Catchments ◽  
Temporal Dimension ◽  
Radar Rainfall ◽  
Additional Constraints

Abstract. Flash flood alerts in metropolitan France are provided by SCHAPI (Service Central Hydrométéorologique et d’Appui à la Prévision des Inondations) through the Vigicrues Flash service, which is designed to work in ungauged catchments. The AIGA method implemented in Vigicrues Flash is designed for flood forecasting on small- and medium-scale watersheds. It is based on a distributed hydrological model accounting for spatial variability of the rainfall and the catchment properties, based on the radar rainfall observation inputs. Calibration of distributed parameters describing these properties with high resolution is difficult, both technically (in terms of the estimation method), and because of the identifiability issues. Indeed, the number of parameters to be calibrated is much greater than the number of spatial locations where the discharge observations are usually available. However, the flood propagation is a dynamic process, so observations have also a temporal dimension. This must be larger enough to comprise a representative set of events. In order to fully benefit from using the AIGA method, we consider its hydrological model (GRD) in combination with the variational estimation (data assimilation) method. In this method, the optimal set of parameters is found by minimizing the objective function which includes the misfit between the observed and predicted values and some additional constraints. The minimization process requires the gradient of the cost function with respect to all control parameters, which is efficiently computed using the adjoint model. The variational estimation method is scalable, fast converging, and offers a convenient framework for introducing additional constraints relevant to hydrology. It can be used both for calibrating the parameters and estimating the initial state of the hydrological system for short range forecasting (in a manner used in weather forecasting). The study area is the Gardon d’Anduze watershed where four gauging stations are available. In numerical experiments, the benefits of using the distributed against the uniform calibration are analysed in terms of the model predictive performance. Distributed calibration shows encouraging results with better model prediction at gauged and ungauged locations.

On the use of the data- and physics-driven approaches for quasi-geostrophic double-gyre problem: application of Genetic Programming

2021 ◽  
Author(s):  
Elnaz Naghibi ◽  
Elnaz Naghibi ◽  
Sergey Karabasov ◽  
Vassili Toropov ◽  
Vasily Gryazev
Keyword(s):  
Genetic Programming ◽  
Data Driven ◽  
Time Signal ◽  
Reduced Order ◽  
Multi Scale ◽  
Data Driven Approach ◽  
Auto Correlation Function ◽  
Gyre Circulation ◽  
Proper Orthogonal ◽  
Double Gyre

<p>In this study, we investigate Genetic Programming as a data-driven approach to reconstruct eddy-resolved simulations of the double-gyre problem. Stemming from Genetic Algorithms, Genetic Programming is a method of symbolic regression which can be used to extract temporal or spatial functionalities from simulation snapshots.  The double-gyre circulation is simulated by a stratified quasi-geostrophic model which is solved using high-resolution CABARET scheme. The simulation results are compressed using proper orthogonal decomposition and the time variant coefficients of the reduced-order model are fed into a Genetic Programming code. Due to the multi-scale nature of double-gyre problem, we decompose the time signal into a meandering and a fluctuating component. We next explore the parameter space of objective functions in Genetic Programming to capture the two components separately. The data-driven predictions are cross-compared with original double-gyre signal in terms of statistical moments such as variance and auto-correlation function.</p><p> </p>

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