Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time

Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management

[Extract] With the advent of computers a wide range of mathematical and numerical models have been developed with the intent of predicting or approximating parts of hyrdrologic cycle. Prior to the advent of conceptual process based models, physical hydraulic models, which are reduced scale representations of large hydraulic systems, were used commonly in water resources engineering. Fast development in the computational systems and numerical solutions of complex differential equations enabled development of conceptual models to represent physical systems. Thus, in the last two decades large number of mathematical models was developed to represent different processes in hydrological cycle

GEP prediction of scour around a side weir in curved channel

Side-weirs have been widely used in hydraulic and environmental engineering applications. Side-weir is known as a lateral intake structure, which are significant parts of the distribution channel in irrigation, land drainage, and urban sewerage system, by flow diversion device. Local scour involves the removal of material around piers, abutments, side-weir, spurs, and embankments. Clearwater scour depth based on five dimensional parameters: approach flow velocity (V1/Vc), water head ratio (h1–p)/h1, side-weir length (L/r), side-weir crest height (b/p) and angle of bend θ. The aim of this study is to develop a new formulation for prediction of clear-water scour of side-weir intersection along curved channel using Gene Expression Programming (GEP) which is an algorithm based on genetic algorithms (GA) and genetic programming (GP). In addition, the explicit formulations of the developed GEP models are presented. Also equations are obtained using multiple linear regressions (MLR) and multiple nonlinear regressions (MNRL). The performance of GEP is found more influential than multiple linear regression equation for predicting the clearwater scour depth at side-weir intersection along curved channel. Multiple nonlinear regression equation was quite close to GEP, which serve much simpler model with explicit formulation. First published online: 17 Mar 201

Using Genetic Programming to Predict Scour Downstream Rapid Hydraulic Structures from Experimental Results

Experimental Studie

Proceedings of the International Workshop on Hydraulic Design of Low-Head Structures - IWLHS 2013

Scientific standards applicable to publication of BAWProceedings: http://izw.baw.de/publikationen/vzb_dokumente_oeffentlich/0/2020_07_BAW_Scientific_standards_conference_proceedings.pd

Prediction of River Discharge by Using Gaussian Basis Function

For design of water resources engineering related project such as hydraulic structures like dam, barrage and weirs river discharge data is vital. However, prediction of river discharge is complicated by variations in geometry and boundary roughness. The conventional method of estimation of river discharge tends to be inaccurate because river discharge is nonlinear but the method is linear. Therefore, an alternative method to overcome problem to predict river discharge is required. Soft computing technique such as artificial neural network (ANN) was able to predict nonlinear parameter such as river discharge. In this study, prediction of river discharge in Pari River is predicted using soft computing technique, specifically gaussian basis function. Water level raw data from year 2011 to 2012 is used as input. The data divided into two section, training dataset and testing dataset. From 314 data, 200 are allocated as training data and the remaining 100 are used as testing data. After that, the data will be run by using Matlab software. Three input variables used in this study were current water level, 1-antecendent water level, and 2-antecendent water level. 19 numbers of hidden neurons with spread value of 0.69106 was the best choice which creates the best result for model architecture after numbers of trial. The output variable was river discharge. Performance evaluation measures such as root mean square error, mean absolute error, correlation of efficiency (CE) and coefficient of determination (R2) was used to indicate the overall performance of the selected network. R2 for training dataset was 0.983 which showed predicted discharge is highly correlated with observed discharge value. However, testing stage performance is decline from training stage as R2 obtained was 0.775 consequently presence of outliers have affect scattering of whole data of testing and resulted in less accuracy as the R2 obtained much lower compared to training dataset. This happened because less number of input loaded into testing than training. RMSE and MSE recorded for training much lower than testing indicated that the better the performance of the model since the error is lesser. The comparison of with other types of neural network showed that Gaussian basis function is recommended to be used for river discharge prediction in Pari river

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Mapping erosion risk at the basin scale in a Mediterranean environment with opencast coal mines to target restoration actions

34 páginas, 9 figurasRiver basin restoration and management is crucial for assuring the continued delivery of ecosystem services and for limiting potential hazards. Human activity, whether directly or indirectly, can induce erosion processes and drastically change the landscape and alter vital ecological functions. Mapping erosion risk before future restoration-management projects will help to reveal the priority areas and develop a hierarchy ordered according to need. For this purpose, we used the Revised Universal Soil Loss Equation (RUSLE) erosion model. We also applied a novel technique called GPVI (Genetic Programming Vegetation Index) in the Martín River basin in NE Spain (2,112 km2), which has a large coalfield located in the southern part of the basin. Approximately two-thirds (69%) of the area of the Martín basin presents low and medium soil loss rates, and one-third (31%) of the area presents high (18%), very high (10%), and irreversible (3%) erosion rates. The southern part of the basin is the most degraded and is strongly influenced by the topography. This work allows us to locate areas prone to erosional degradation processes to help create a buffer around the river and locate “spots” in need of restoration. We also checked the error estimation of the methodology because our soil maps do not include rock and bare rock areas. The usefulness of applying RUSLE for predicting degraded areas and the consequent directing of soil conservation–restoration actions at the basin scale is demonstrated. We highly recommend a field survey of the selected areas to prove the goodness of the model estimations.This work is part of the research and assistance agreement between Endesa S.A. and CSICPyrenean Institute of Ecology (IPE-CSIC). Funding for this study was provided by Endesa S.A. A special acknowledge is given to Endesa Centro Minero Andorra (Teruel). Thanks are given to, J. M. Garcia Ruiz, S. Begueria, E. Nadal, E. Moran-Tejera, and J.J. Jimenez for reviewing and general advises during the development of this work, M. P. Errea, J. Zabalza, L. C. Alatorre for assistance with GIS analysis, M. Angulo for R factor map, M. Pazos with statistical analysis, and F. Reverberi for laboratory work. M. Trabucchi was in receipt of grant from JAE-CSIC (Ref. I3P-BPD-2006).Peer reviewe