Inverse modelling of the rainfall-runoff relation: A multi-objective model calibration approach

Civil Engineering and Geoscience

Multiobjective training of artificial neural networks for rainfall-runoff modeling

This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for this purpose. The performances of the multiobjective algorithms Multi Objective Shuffled Complex Evolution Metropolis–University of Arizona (MOSCEM-UA) and Nondominated Sorting Genetic Algorithm II (NSGA-II) have been compared to the single-objective Levenberg-Marquardt and Genetic Algorithm for training of these models. Performance has been evaluated by means of a number of commonly applied objective functions and also by investigating the internal weights of the networks. Additionally, the effectiveness of a new objective function called mean squared derivative error, which penalizes models for timing errors and noisy signals, has been explored. The results show that the multiobjective algorithms give competitive results compared to the single-objective ones. Performance measures and posterior weight distributions of the various algorithms suggest that multiobjective algorithms are more consistent in finding good optima than are single-objective algorithms. However, results also show that it is difficult to conclude if any of the algorithms is superior in terms of accuracy, consistency, and reliability. Besides the training algorithm, network performance is also shown to be sensitive to the choice of objective function(s), and including more than one objective function proves to be helpful in constraining the neural network training.Water ManagementCivil Engineering and Geoscience

Geostatistics and Hydrology: Part 3: Hydro-Meteorological Network Design

At present, the collection of environmental information is increasing in importance. Environmental modelling and defining measures relating to environmental protection policies are usually taken on the basis of the collected information. Especially in the more developed countries, hydrologists and meteorologists are in this respect privileged since often long records of hydrological information exist. Hydrological information like precipitation data, runoff data, groundwater table data, piezometric data, etc., is gathered and stored throughout the years by monitoring gauging stations of a (well designed) network. In developing countries however gauging stations are often badly spatially distributed, not well managed and often not included in a network. This causes regions sometimes to be 'over-gauged' by independent (interrelated) agencies or at the other hand regions sometimes to be undergauged or not monitored at all. Many water resources management projects are (still) designed with inadequate and incorrect data or even with virtually no data at all. As a consequence of this, it is likely that wrong water management decisions will be taken, that wrong design criteria will be applied, and that inappropriate and uneconomic designs will be developed and operated. The designing of efficient and economic networks is therefore an important issue for hydrologists and civil engineers. The system or network of hydrological gauging stations provides the necessary information to be able to understand and to describe the hydrological phenomena and processes under study.Water ManagementCivil Engineering and Geoscience

Raingauge Network Optimization and Gis: A Case Study of the Mananga Basin

The study area of this case study is the river basin of the Mananga river, one of the main rivers on Cebu-island, the Philippines. Today the Mananga is used as discharge river, in the near future this river may be used for drinking water purposes of the region around Cebucity, some 25 kilometres east of the Mananga catchment. This catchment area comprises about 102 square kilometres, and is surrounded by a chain of sharp-edged mountains which reach to a maximum height of 700 meter. The total length of the Mananga-river is 24.5 kilometres, the drainage area upstream Camp-4 is about 64 km2 (see the front page figure). According to the Köppen classification the elimate of Cebu is classified as TC; tropical characterized by high temperatures and humidity, and heavy rainfall. There are, however, pronounced regional and, in most parts, seasonal variations. Average annual rainfall ranges from 1300 to 1700 mm; average annual temperatures ranges from 26°C to 29°C.Water ManagementCivil Engineering and Geoscience

Constraints of artificial neural networks for rainfall-runoff modelling: Trade-offs in hydrological state representation and model evaluation

International audienceThe application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of the Geer catchment (Belgium) using both daily and hourly data. The daily forecast results indicate that ANNs can be considered good alternatives for traditional rainfall-runoff modelling approaches, but the simulations based on hourly data reveal timing errors as a result of a dominating autoregressive component. This component is introduced in model simulations by using previously observed runoff values as ANN model input, which is a popular method for indirectly representing the hydrological state of a catchment. Two possible solutions to this problem of lagged predictions are presented. Firstly, several alternatives for representation of the hydrological state are tested as ANN inputs: moving averages over time of observed discharges and rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these hydrological state representers produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the possibility of using multiple measures of model performance during ANN training is mentioned

Network optimization: A simple approach applying GIS and MLR

The collection of information (data) of a specific kind, in space and/or time is of major importance to the hydrologist. On regional scale, measurements of precipitation at several locations provide information of the input of a river catchment system. The study area with dimensions of 60 * 55.4 km2 , comprises 16 precipitation stations establishing a network. In general one can say that many experiments have been performed on gauged regions and many suggestions have been made relating the desired accuracy with the distance between the stations and the density of the network with the relief (WMO) , the local climatic conditions and the estimation of the study flood for a desired return period. Also it is proved that the increase of the network density is not always the best solution (even though one might not be so interested in the increase of costs). So the real problem is not only quantitative but qualitative as weIl and therefore more complex. This study dealt with the optimization of the existing network applying a mathematical- and a digital technique in combination with operational aspects. Although the influence of the individual approaches is difficult to quantify, conclusions which can be drawn are very satisfying; 2 stations can be abolished, 2 stations need to be re-equipped, for 3 stations the observer needs to be reconsidered. Also possible locations for new stations are distinguished. For composing the optimum network and rainfall-runoff relation ships additional research with more sophisticated digital techniques are requested.Water ManagementCivil Engineering and Geoscience