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

N M Hakmanage,N V Chandrasekara,D D M Jayasundara

doi:10.4038/tar.v32i2.8466

N M Hakmanage, N V Chandrasekara + Show 1 more

Open Access

https://doi.org/10.4038/tar.v32i2.8466

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Journal: Tropical Agricultural Research	Publication Date: Apr 1, 2021
Citations: 1	License type: cc-by

Affiliation: University of Kelaniya

Abstract

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 (R<sup>2</sup>) 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.

Highlights

Climate change is any change in climate over time, whether due to natural variability or as a result of human activity (Praveen and Sharma, 2020)
Forecasting of climatic factors is required to optimize the quantity of the yield that can be achieved under different climate change scenarios
As this study aims at forecasting rainfall using other climatic factors, correlation analysis was carried out between rainfall and other climatic factors using both Pearson’s and Spearman Rho correlation coefficients

Summary

INTRODUCTION

Climate change is any change in climate over time, whether due to natural variability or as a result of human activity (Praveen and Sharma, 2020). The possible effects of climate changes on agricultural production are limited to the crop cultivation and cause fluctuations on quantity of the crop yields. Some of the most important climatic factors that would affect agricultural productivity are rainfall (Neenu et al, 2013), changes in temperature (Hatfield and Prueger, 2015), carbon dioxide fertilization, short term weather variability and surface water run off (Joshi and Amalkar, 2009). Hung and Babel (2009) observed that relative humidity, air pressure, wet bulb temperature, cloudiness are the most important climatic factors which should be considered in rainfall forecasting. Based on these parameters, rainfall forecasting models can be developed using artificial neural networks.

METHODOLOGY

RESULTS AND DISCUSSION

CONCLUSIONS