Integrated ANN Modelling for assesment of Runoff due to Land-Use change using Remote Sensing and GIS
for integrated ANN model for the entire basin as shown in Fig.2. R1
i is monthly rainfall input during i
th month
to ANN1, which is formulated, for subbasin 1. Similarly SM1
i is average moisture condition and L1
k is
percentage land-use of k
th type for subbasin1. Two statistical criteria, namely, root mean square error
(RMSE), and model efficiency (R
2) (Nash and Sutcliffe, 1970) are employed to measure goodness of fit.
These criteria are applied to the model both for training and validation phases. The training parameters are
the maximum number of training epochs, error goal, the learning rate, and the momentum factor. The error
goal is taken as 0.2 in this study. The error gradient and sum square error are observed over the
parameters. RNN is run separately for all subbasins with different hidden neuron numbers..Input Parameters: In the model, there are six inputs namely monthly rainfall, average soil moisture index,
and percentage of four major land-use types (forest, agriculture, settlement and pastureland), and only one
output as monthly runoff. There are seven types of data sets namely (i) monthly runoff observed from land-use
of 1978 (LU1), 1984 (LU2), 1990 (LU3) and 1996 (LU4), (ii) monthly runoff due to superimposed rainfall
pattern of 1978 on LU2, LU3 and LU4, (iii) monthly runoff due to superimposed rainfall pattern of 1978 and
1996 on land-use of future plan scenario, (iv) monthly runoff due to superimposed rainfall pattern of 1978 on
land-uses after assumed deforestation of 5, 10, 15, and 20%, (v) monthly runoff due to superimposed
rainfall pattern of 1978 on land-uses after assumed urbanization of 5, 10, 15,and 20%, (vi) monthly runoff
due to superimposed rainfall pattern of 1978 on land-uses after combined deforestation and urbanization of
5, 10,15 and 20 % on upstream subbasin, and (vii) monthly runoff due to superimposed rainfall pattern of
1978 on land-use after combined deforestation and urbanization of 5, 10,15 and 20 % on downstream
subbasin. Thus the total numbers of input sets are twenty-five. Each set has 12 subsets of data Twenty-two
sets are considered for model training and three sets for validation.
Fig.2. Schematic Representation of Integrated Artificial Neural Network for Kathmandu Valley Basin
Testing or validation sets are of LU3
with rainfall of year 1990, LU4 with rainfall of year 1996 and future plan scenario with rainfall pattern of year
1996. Thus the total number of input data set for training each subbasin is 264 and 36 for validation. All
subbasins have six inputs and subbasins 13 and 14 (Khasyang khusung and Gakhu khola) have five inputs.
The percentages of land-use categories for each subbasin are calculated from land-use map of respective
years. The average soil moisture index is estimated by taking average of daily antecedent moisture
condition estimated according to SCS method (SCS, 1985). The index values are taken as integer after
rounding the average value, i.e. 1,2 and 3 for AMC I, II and III respectively for all the days of the month.
The model is applied for new set of assumed urbanization of 3%, 18% and 30%. Rainfall pattern of 1978 is
superimposed to determine the runoff from these urbanized land-uses. The changed land-use is then
overlaid with soil map. HSUs are delineated using Arc/Info after carefully study of possible surrounding area
of each HSU for land use change. Overlaying with Thiessen polygon map, the rainfall depth is computed for
each HSU. The runoff at outlet of subbasins and outlet of the basin are calculated for three different sets of
urbanization by DHM (Shrestha, 2001). The new input sets are used in both ANN models for each
subbasin. Using weight and bias matrix of the trained model, the normalized outputs are obtained from
individual subbasins ANN model separately. Then these are taken as input in integrated ANN models.
Analysis of Results
There are four types of HSG, 7 types of land use and five range of slope in the study area. Considering
different combinations, twenty-five types of HSUs are derived (Mohan and Shrestha, 2000). HSU type 11
and 15 are not found in the area. Total numbers of HSUs derived are 1119, 1291, 1375 and 407 for 1978,
1984, 1990 and 1996 respectively. The minimum delineation of area observed is of 8 m 2 . The calculated
daily runoff values at the outlet of the basin are found close with observed daily runoff during monsoon
season and slightly lower during non-monsoon season. The monthly average rainfall over the basin is
calculated with Thiessen coefficients derived from Thiessen Polygon coverage. The summation of product of
HSU area and CN is divided by the total basin area to calculate three types weighted CN as per AMC. The
weighted CNs are 44.87, 61.41 and 76 for the year 1978, 45.17, 61.78, 76.47 for year 1984, 47.66, 64.61,
78.99 for year 1990 and 47.83, 64.8 and 79.2 for year 1996 for AMC I, II and III respectively. Percentage
area and distribution of each HSU for different years are shown in Fig.3.
Fig.3. Distribution of Hydrological Similar Unit (HSU) for estimated Defferent Landuses Models
