**3. Study area and data**

258 Genetic Programming – New Approaches and Successful Applications

about sixth in the world today.

**2. Literature review** 

follows.

The aim of this chapter is to predict suspended sediment load of a river into the future. Besides the traditional empirical Sediment Rating Curve (SRC), there are several strategies for analysing such time series and *evolutionary computing* is one of Artificial Intelligence (AI) approaches, which broadly include capabilities for searching and recognising patterns among others. This chapter also employs Artificial Neural Network (ANN), which is another AI approach. Yet another strategy is to regard time series as outcomes of many random drivers and this assumption is supported by a whole body of probabilistic approaches, where this chapter uses Multi-Linear Regression (MLR) analysis to model the same data. Over the past few decades, research has increasingly focused on the application of deterministic chaos (or chaos theory or dynamic systems) showing that many of apparently randomly varying system behaviours can be explained by deterministic chaos. The concept behind this modelling strategy is that the particular data can largely be explained by deterministic behaviour, where in time the system evolves asymptotically towards an *attractor*. Its random-looking variations are assumed to be an internal feature of the system and depending on its initial conditions, its state under a certain range may become highly erratic but with a predictable behaviour. Evidently, none of these strategies are identical and different models rarely produce identical results. This chapter therefore compares the performance of these modelling strategies for solving an engineering problem. The study employs 26 years of the Mississippi River data recorded at Tarbert + RR Landings and involve both flows and suspended sediment load. The river discharges about 200 million metric tons of suspended sediment per year to the Gulf of Mexico, where it ranks

Sediment Rating Curve (SRC) is an empirical approach used by practitioners in the engineering studies of sediment and erosion problems. The log linear rating curve method has been used widely and Sivakumar and Wallender (2005) outline the many flaws associated with this technique, including the lack of fit due to missing variables (e.g. Miller, 1951), retransformation bias (e.g. Ferguson, 1986), and non-normality of the error distribution (e.g. Thomas, 1988). According to Sivakumar and Wallender (2005), the technique has been modified including, among others, use of separate curves for different seasons (Miller, 1951), stratifying the data according to the magnitude of flow and applying a separate curve for each stratum (Glysson, 1987), and use of a single multivariate model instead of multiple rating curves (Cohn *et al.*, 1992). Sivakumar and Wallender (2005) argue that there is not a simple (and universal) 'water discharge-suspended sediment concentration-suspended sediment load' relationship. A brief overview of past studies is as

Kisi, et al (2008) review the application of ANN and neuro-fuzzy techniques to time series analysis of sediment loads at various timescales, uncertainty in the data. Variations of these techniques have also been reported by Jain (2001), Tayfur (2002), Cigizoglu (2004), Kisi (2004), Raghuwanshi *et al*. (2006), Cigizoglu & Kisi (2006). Other studies on the application
