Application of Artificial Neural Networks for Rainfall-runoff Modelling

Rainfall-Runoff models are mostly empirical in nature demanding the knowledge of a large number of catchment parameters. On the contrary Artificial Neural Networks (ANN) can be deployed in cases where the available data is limited. The present work involves the development of an ANN model using Backward Propagation algorithm. The hydrologic variables used were Rainfall, Soil Moisture, Evaporation and Runoff of monsoon months for a specific period. Model 1 involved the training of the ANN model with Rainfall values only, the output being the Runoff. Whereas, in Model 2 the training set consisted of Rainfall, Soil Moisture and Evaporation values, Runoff being the desired output. Effect of number of layers in the network is also studied. A comparison of the performance of the two models is carried out. The model yielding the least error is recommended for simulating the Rainfall-Runoff characteristics of the watershed. The ANN model is applied to Lekkur watershed in Tamilnadu for which the hydrologic data were available for 9 years. With the developed ANN model Runoff values were predicted and they compared well with the observed values. INTRODUCTION Neurons are nerve cells and neural networks are networks of these cells. The cerebral cortex of the brain is an example of a natural neural network. Somehow, such a network of neurons thinks, learns, feels and remembers. Many attempts had been made in the past to build models to study such neural networks. There are two major types of models biological and technological. In biological modelling the goal is to study the structure and function of real brain in order to explain biological aspects such as behaviour. In technological modelling the goal is to study brains in order to extract concepts to be used in new computational methodologies. The latter viewpoint is taken by several investigators working in the area of artificial neural networks and nurocomputers (Hecht-Nielsen, 1988) The artificial neural network (ANN) approach differ from the traditional approaches in stochastic hydrology in the sense that it belongs to a class of data-driven approaches as opposed to traditional model driven approaches. In this paper, a neural network computer program was developed to carry out Rainfall-Runoff modelling of Lekkur catchment area in Tamilnadu. The neural network was developed using the generalized delta rule for a semi-linear feed forward net with error back propagation. The program code was written in C in UNIX environment. The neural network model was treated as a Black Box, as the relationships between the physical components of the catchment were not to be fed. Model 1 involved the training of the ANN model with Rainfall values only, the output being the Runoff. Whereas, in Model 2 the training pairs consisted of Rainfall, Soil Moisture and Evaporation values, Runoff being the desired output. oj 1 1 e (netj j)/ 0 COMPUTATIONS IN ANN The computational process associated with an ANN is as follows: An artificial neuron (AN) receives its inputs from a number of other ANs or from the external world (Lippmann, 1987). A weighted sum of these inputs constitutes the argument of an activation function. This activation function is assumed to be nonlinear. Hard limiting threshold, i.e., either the step or signum function, and soft limiting threshold, i.e. sigmoidal, are the three most often used forms of non-linearities. The resulting value of the activation function is the output of the AN. This output is distributed along the weighted connections to other ANs. The components of an input pattern constitute the inputs to the node in layer i. The outputs of the nodes in that layer may be taken to be equal to the inputs. The net input to a node in layer j is