Early Recognition of Rainfall to Plan and Manage the Cultivation: A Forecasting Model Using Artificial Neural Networks for Dompe Divisional Secretariat in Gampaha District, Sri Lanka

As the agricultural productivity is climate sensitive, forecasting climatic factors is important to maximize the harvest and to planning and management of the cultivation. Even though all the climatic factors have influence on cultivation, rainfall is one of the major influential factor to Sri Lankan agriculture. Rainfall forecasting is vital in agriculture and it is a challenging task due to the uncertainty of natural phenomena. Artificial neural network (ANN) approach is being applied for forecasting real time rainfall using climatic factors affecting to rainfall. The aim of this study was to identify a neural network model which is capable of forecasting rainfall of Dompe Divisional Secretariat in Gampaha District with a high accuracy. Feed forward neural network model (FFNN) was applied with Levenberg-Marquardt back propagation algorithm and the network parameters were adjusted to minimize the forecasting error. The suggested FFNN model consisted of 12 input variables with two hidden layers with 13 and 10 hidden neurons in first and second layers, respectively. Log sigmoid transfer function used in hidden layer 1 and 2 while pure linear transfer function was used in the output layer. The model forecasts rainfall with mean squared error of 0.0895 and normalized mean squared error of 0.1975. The coefficient of determination (R²) of the testing set was 0.8. These results demonstrated the suitability of other climatic factors: temperature, wind speed, air pressure, humidity, percentage of clouds and their lags in forecasting rainfall using ANN technique in forecasting rainfall with high accuracy.