Satellite Data Processing
Remote sensing data usually contain both systematic and nonsystematic geometric errors. These errors may be divided into two classes: those that can be corrected using data from platform ephemeris and knowledge of internal sensor distortion, and those that cannot be corrected with acceptable accuracy without a sufficient number of ground control points (Jensen, 1986)

Geometric Correction
Geometric correction is undertaken to avoid geometric error from a distorted image. In this study, the Landsat-5 TM image was rectified using ground control point (GCP). The GCPs were taken from topographical map of the study area. Cubic convolution resampling technique was used in the geometric correction which results in sharpening as well as smoothing the image. Thirteen GCPs were used in the geometric correction which produced root-mean-square error of about 10 meters in Easting and Northing.

Image Classification
Image classification was carried out to classify the land use type in the study area. This information is required so that specific Curve Number (CN) can be assigned to the specific land use in the hydrological modelling described in section 5.0. The supervised classification technique using the maximum likelihood classifier was used. In a supervised classification, the identity and location of some of the land cover type, such as urban, agriculture, wetland and forest are known a priori through a combination of field work, analysis of aerial photography, maps and personal experience (Jensen 1986). In this study, the training areas for supervised classification were identified from topographic maps and existing land use maps. Ten classes of land cover have been identified in this study area, namely, (1) mangrove, (2) urban or built up areas, (3) oil palm plantation, (4) coconut plantation, (5) forest, (6) open areas, (7) rubber plantation, (8) paddy, (9) water body and (10) grassland. The overall classification accuracy is about 86%.

Hydrological Modelling
In this study the U.S Soil Conservation Service Technical Release 55 (SCS TR-55) hydrologic model has been used to predict floods in the Klang Valley and its surrounding areas. This model presents a simplified procedure for estimating runoff and peak discharge in small watersheds (U.S. Department of Agriculture, 1986). There are several calculations involved that include the determination of runoff by SCS TR-55 Curve Number (CN) method, concentration time, peak discharge, and bankfull discharge.

The Determination of Runoff
The U.S. SCS TR-55 method uses the Curve Number method to estimate runoff from storm rainfall. This method starts with the determination of CN, which depends on the watershed's soil and cover conditions. The watershed's soil and cover conditions in SCS TR-55 model represent the hydrologic soil group, cover type, treatment and hydrologic condition.

The SCS TR-55 runoff equation used is : -

where, Q = runoff (in)
P = rainfall (in)
s = potential maximum retention after runoff begins (in)
I_a = Initial abstraction (in).
Initial abstraction is all losses before runoff begins. Through studies of many small agricultural watersheds, Ia was found to be approximated by the following empirical equation (U.S. Department of Agriculture, 1986) : -
I_a= 0.2s…………………(2)
By substituting the equation (2.) into equation (1.), gives : -

s is related to the soil and cover conditions of watershed through CN and s related to CN by : -

Based on the SCS TR-55 model, the Runoff Curve Number for the watershed's land cover, soil type and conditions in the study area is given in Table 3.
TABLE 3. CN for each land cover in study area

Land Cover / Land Use	Curve Number (CN), for Hydrological Soil Group - B
Water Body	100
Open Area	79
Mangrove	98
Oil Palm	60
Coconut	65
Rubber	66
Forest	55
Urban or Built up area	93
Paddy	79
Grassland	65

In this calculation a rainfall amount of 3.94 in was used based on 24-hour storm event for the study area. The runoff values (Q) estimated by SCS TR-55 CN method for each watershed in the study area is given in Table 4 whilst the watersheds are showed in Figure 4.
FIGURE 4. Watersheds in the study area.


TABLE 4. Runoff for each watershed

Watershed	Runoff (Q) (in)
Wt1	0.8775
Wt2	0.8369
Wt3	0.8990
Wt4	0.8478
Wt5	0.9957
Wt6	0.9811
Wt7	1.7806
Wt8	1.4120
Wt9	2.2154
Wt10	1.3406
Wt11	2.5235
Wt12	1.9504
Wt13	2.2750
Wt14	1.4303
Wt15	1.8968
Wt16	1.3493
Wt17	1.2351
Wt18	1.8337
Wt19	1.8855
Wt20	2.0648
Wt21	1.6273
Wt22	1.4428
Wt23	1.1375