No abstract

Governments face the daunting task of developing policies and making investment decisions for climate change adaptation in an environment that consist of complex, interlinked systems with manifold uncertainties. Instead of responding to surprises and making decisions on ad [ 1 7 _ T D $ D I F F ] hoc basis, a structured approach to deal with complex systems and uncertainties can provide indispensable support for policy making. This contribution proposes a structured approach for designing climate adaptation policies based on the concepts of Adaptation Pathways, Adaptive Policy Making, and Real Options Analysis. Such an approach results in incorporation of flexibility that allows change over time in response to how the future unfolds, what is learned about the system, and changes in societal preferences. The approach is illustrated by looking at drainage policies and measures to address flooding in Singapore.

This study demonstrates a combined application of chaos theory and support vector machine (SVM) in the analysis of chaotic time series with a very large sample data record. A large data record is often required and causes computational difficulty. The decomposition method is used in this study to circumvent this difficulty. The various parameters inherent in chaos technique and SVM are optimised, with the assistance of an evolutionary algorithm, to yield the minimal prediction error. The performance of the proposed scheme, EC-SVM, is demonstrated on two daily runoff time series: Tryggevælde catchment, Denmark and the Mississippi River at Vicksburg. The prediction accuracy of the proposed scheme is compared with that of the conventional approach and the recently introduced inverse approach. This comparison shows that EC-SVM yields a significantly lower normalised RMSE value of 0.347 for the Tryggevælde catchment runoff and 0.0385 for the Mississippi River flow compared to 0.444 and 0.2064, respectively, resulting from the conventional approach. A slight improvement in accuracy was obtained by analysing the first difference or the daily flow difference time series. It should be noted, however, that the computational speed in analysing the daily flow difference time series is significantly much faster than that of the daily flow time series.

Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real‐time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall‐runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real‐time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly.