**3.3. Review of data records**

Daily suspended sediment measurements for the above station have been made available by the USGS from April 1949. The data used herein span over a period of about 26 years (amounting to 9496 datapoints) starting on October 1, 1949. Figures 2 show the variation of the daily suspended sediment and stream flow series observed at the above station.

Of the 26 water-years of the data sample of daily records of flow and suspended sediment (9496 datapoints), the first 25 water years of data (9131 datapoints) were used to train the models and the remaining 365 datapoints of daily records were used for testing. The statistical parameters of stream flow and sediment concentration data are shown in Table 1. These results show that the overall contribution of the datapoints in the test period is average; its individual characteristics in terms of kurtosis show the annual hydrographs to be less peaked and more flat but at the same time, the suspended sediment load during the year is significantly high. Thus, the minimum values during this year were significantly above the average but persistent and though less dynamic.

**Figure 1.** Mississippi River Station at Tarbert + RR Landings

The flow–sediment time series data of a Mississippi river Station is used in the study, the location of which is shown in Figure 1. The gauge is situated at Tarbert + RR Landings, LA (USGS Station no. 07373291, latitude 30°57′40″, longitude 91°39′52″) and it is operated by the US Geological Survey (USGS) – the location map is shown in Figure 1. The Mississippi River discharges an average of about 200 million metric tons of suspended sediment per year to

Daily suspended sediment measurements for the above station have been made available by the USGS from April 1949. The data used herein span over a period of about 26 years (amounting to 9496 datapoints) starting on October 1, 1949. Figures 2 show the variation of

Of the 26 water-years of the data sample of daily records of flow and suspended sediment (9496 datapoints), the first 25 water years of data (9131 datapoints) were used to train the models and the remaining 365 datapoints of daily records were used for testing. The statistical parameters of stream flow and sediment concentration data are shown in Table 1. These results show that the overall contribution of the datapoints in the test period is average; its individual characteristics in terms of kurtosis show the annual hydrographs to be less peaked and more flat but at the same time, the suspended sediment load during the year is significantly high. Thus, the minimum values during this year were significantly

the daily suspended sediment and stream flow series observed at the above station.

above the average but persistent and though less dynamic.

(http://pubs.usgs.gov/circ/circ1133/geosetting.html).

the Gulf of Mexico and to the ocean.

**3.3. Review of data records** 

**3.2. Study area** 

**Figure 2.** Variation of Daily Suspended Sediment and Flow Data in the Mississippi River Basin


Inter-Comparison of an Evolutionary Programming

Model of Suspended Sediment Time-Series with Other Local Models 263

Sediment rating-curve is a flux-averaged relationship between suspended sediment, *S*, and water discharge, *Q*, expressed as a power law in the form of: *<sup>b</sup> S aQ* , where *a* and *b* are coefficients. Values of *a* and *b* for a particular stream are determined from data via a linear regression between (log *S*) and (log *Q*). The SRC model is represented in terms of Model 1 in Table 2. For more critical views on this model, references may be made Kisi (2005) and

Evolutionary computing techniques apply optimisation algorithms as a tool to facilitate the mimicking of natural selection. A building block approach to generalised evolution driven by natural selection is yet to be presented, although Khatibi (2011) has outlined a rationale for it. Traditional understanding of natural selection for biological species is well developed, according to which the process takes place at the gene level of all individuals of all species carrying hereditary material for reproduction by inheriting from their parents and by passing on a range of their characteristics to their offspring. The process of reproduction is never a perfect copying process, as mutation may occur from time to time in biological reproductions involving the random process of reshuffling the genes during sexual reproduction. The paper assumes preliminary knowledge on genes, chromosomes, gene pool, DNA and RNA, where the environment also has a role to play. The environment for the production of proteins and sexual reproduction is different than the outer environment for the performance of the individual entities supported by the proteins or produced by sexual reproduction. The outer environment is characterised by (i) being limited in resources, (ii) having no foresight, (iii) organisms tend to produce more offspring than can be supported, a process that is driven by positive feedback loops, and (iv) there is a process of competition and selection. Some of these details are normally overlooked or simplified in evolutionary computing and therefore the paper stresses the point that natural selection takes place at the gene level and this is not directly applicable to that at the social level.

Facts on natural selection are overwhelming but there are myths as well, e.g. the myth of "the survival of the fittest" and this term is widely used in evolutionary computing. Although the fittest has a selective advantage to survive, this is not a guarantee for the survival in the natural world. An overview of the dynamics of natural selection in an environment is that (i) the environment can only support a maximum population of certain size, but there is also a lower size at the critical mass below which a population is at risk of losing its viability; (ii) there is a process of reproduction, during which natural selection operates at the gene level, although there are further processes operating at the individual levels beyond the direct reach of natural selection (e.g. interactions among the individuals catered for by other mechanisms or each individual is under selection pressure by the environment); (iii) the process of reproduction is associated with mutation, which gives rise

*3.4.1. Sediment rating curve* 

Walling (1977), among others.

*3.4.2. Evolutionary computing* 

to the production of gene pools.

\*Data = Number of Data; Std = Standard Deviation; Max = Maximum Value; Min = Minimum Value; CV = Coefficient of Variation; Skew = Skewness; Kurt = Kurtosis

**Table 1.** Statistical Parameters for Dataset from the Mississippi River Basin
