This paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using Gene Expression Programming (GEP) to perform symbolic regression. The GEP multi-model combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments.The four selected models for the multi-model combinations are the Linear Perturbation Model (LPM), the Linearly Varying Gain Factor Model (LVGFM), the Soil Moisture Accounting and Routing (SMAR) Model, and the Probability-Distributed Interacting Storage Capacity (PDISC) model. The first two of these models are 'black-box' models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical location, hydrological and climatic conditions, are used to test the performance of the GEP combination method.The results of the model using GEP method are compared against original forecasts obtained from the individual models that contributed to the development of the combined model by means of a few global statistics. The findings show that a GEP approach can successfully used as a multi-model combination method. In addition, the GEP combination method also has benefit over other hitherto tested approaches such as an artificial neural network combination method in that its formulation is transparent, can be expressed as a simple mathematical function, and therefore is useable by people who are unfamiliar with such advanced techniques. The GEP combination method is able to combine model outcomes from less accurate individual models and produce a superior river flow forecast.
Parameter optimisation is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall-runoff processes. This study presents two modifications to achieve optimised results for a Tank Model in less computational time.
This paper summarises a literature review undertaken for a project to develop information visualisation of participatory land use management (PLUM) in New Zealand. No examples were found where information visualisation and/or a participatory approach had been used in the creation of a Land Management System (LMS) for New Zealand. However, some of the relevant participatory methods applied elsewhere have been integrated in this paper to develop a novel strategy for managing New Zealand parks and reserves.
Ongoing research on the use of data-driven techniques for rainfall-runoff modelling and forecasting has stimulated our desire to compare the effectiveness of transparent and black-box type models. Previous studies have shown that models based on Artificial Neural Networks (ANN) provide accurate blackbox type forecasters: whilst Gene Expression Programming (GEP: Ferreira, 2001; 2006) provides transparent models in which the relationship between the independent and the dependant variables is explicitly determined. The study presented in this paper aims to advance our understanding of both approaches and their relative merits as applied to river flow forecasting. The study has been carried out to test the effectiveness of two forecasting models: a GEP evolved equation and a model that uses a combination of ANN and Genetic Algorithms (GA). The two approaches are applied to daily rainfall and river flow in the Blue Nile catchment over a five year period. GeneXproTools 4.0, a powerful soft computing software package, is utilised to perform symbolic regression operations by means of GEP and in so doing develop a rainfall-runoff forecasting model based on antecedent rainfall and river flow inputs. A transparent model with independent variables of antecedent rainfall and flow to forecast river discharge could be achieved. The ANN model is developed with the assistance of a GA: the latter being used in the selection of the ANN inputs from a predetermined set of external inputs. The rainfall and flow data for the first four years was used to develop the model and the final year of data was used for testing. The paper describes the methods used for the selection of inputs, model development and then compares and contrasts the two approaches and their suitability for river flow forecasting. The results of the study show that the GEP model is a useful transparent model that is superior to the ANN-GA model in its performance for riverflow forecasting.
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