Integrated ANN Modelling for assesment of Runoff due to Land-Use change using Remote Sensing and GIS
Methodology
Development of Hydrological Model: The method for evaluating the change in runoff value due to land
use changes can be implemented by integrating remote sensing, GIS and ANN Model. In this study, the
computational elements of ‘Hydrological Similar Units’ (HSU) (Ott et al, 1991) are considered with accurate
mapping of land-use at micro level using remote sensing and GIS. Three-dimensional physiographic
heterogeneity in terms of topography, soil and land use can be grouped together into associations. These
associations are defined here as Hydrological Similar Units. HSUs are areas with same land use, same
pedo-topo-geological conditions controlling their unique hydrological dynamics. The widely accepted, the
SCS-CN technique is adapted here to compute the runoff from the several HSUs of the basin for the given
rainfall. The runoff from each individual HSUs are then routed and the total runoff resulted from the basin is
computed for the given rainfall using Muskingum routing method by HEC-I model. Land use, hydrologic soil
group (HSG) and slope coverages are overlaid using Arc/Info to delineate the HSUs. Appropriate curve
numbers (CNs) are assigned to each HSU considering antecedent moisture conditions (AMC). Then the
direct runoff values from each HSU are estimated using SCS-CN method for rainfall events. Effectof land
use changes are evaluated for different periods by quantifying the runoff. The changes in land use are
evaluated by accounting the HSU distribution over the entire area for different periods and which in turn,
gives the changes in CN over the period considered.
Development of ANN model: The relationship between the changes of the runoff values for the change in
rainfall was found to be non-linear for different land-use (Shrestha, 2001) For efficient mapping of non-linear
rainfall-runoff pattern, the artificial Neural Network (ANN) models are developed and these neural networks
can be used as a decision-making tool to assess change in runoff due to different land-use changes. Two
types of the ANN models namely Feed forward error back propagation network (FFBPN) (Rumelhart et al,
1986) and Recurrent Neural Network (RNN) as feedback ANN (Elman, 1990) are adapted. These models
are developed separately for each subbasins of the study area. The training of the model is accomplished
by providing inputs to the model, computing the output and adjusting the interconnection weights until the
desired outputs are obtained. When the training is completed, the weight for each interconnection is known
and remains fixed for the particular network. The network architecture that resulted in the minimum error
over the training epochs is adopted as the optimal architecture. All the input data are normalized. Using
optimum network architecture, the ANN models are trained for given input and output sets separately. The
modeled output values are then compared with target output. The values are then examined by 95 % level
of confidence. To check the scatter of the values, þ10 % deviation band is used.
Final weights and bias values calculated during training phase for the network are used for validation phase.
The validation involves evaluating the network performance on a set of test problems that were not used for
training. The models with defined architecture during training are run for va lidation separately for all the
subbasins. The output is compared with target for each subbasin. All sets of monthly-normalized output
(runoff) from each ANN models (FFBPN and RNN) designed for the subbasins are considered as input set
