Abstract
In developing countries such as Rwanda whose economy depends on agriculture and where more than 70% of the population relies on rain-fed agriculture for their livelihoods and which is among the highly populated countries in the world, changes in temperature, precipitation, humidity and arable land extremely affect the agricultural production. In response to these changing risks, forecasting cereals production study based on regression and back-propagation (BP) network model was carried out in Eastern Province of Rwanda. 22 years data from 1989 to 2010 of temperature, precipitation, humidity, percentage of cultivated area and cultivated area were taken as dependent variables and cereals production in the same period was considered as independent variable for the regression, while temperature, precipitation, humidity, percentage of cultivated area and cultivated area constituted the input variables to build BP Network model for forecasting cereals production. The model was consistently verified; results were efficient and showed that the general trend of cereals production in Eastern Province of Rwanda is increasing. Key words: Cereals production, regression, back-propagation (BP) Network model, forecasting, Eastern Province of Rwanda.
Highlights
As reported by Hertz et al (1991), artificial neural networks (ANNs) are used to solve a wide variety of problems in science and engineering, for some areas where the conventional modeling methods fail, and a well-trained ANN can be used as a predictive model for a specific application, which is a dataprocessing system inspired by biological neural system.The neurons can be classified into three types: input, output and hidden; input neurons are the ones that receive input from the environment, output neurons are those that send the signals out of the system and neurons which have inputs and outputs within the system are called hidden neurons (Poursina, 2008; Zhou and Kang, 2005)
In response to these changing risks, forecasting cereals production study based on regression and back-propagation (BP) network model was carried out in Eastern Province of Rwanda. 22 years data from 1989 to 2010 of temperature, precipitation, humidity, percentage of cultivated area and cultivated area were taken as dependent variables and cereals production in the same period was considered as independent variable for the regression, while temperature, precipitation, humidity, percentage of cultivated area and cultivated area constituted the input variables to build BP Network model for forecasting cereals production
The results proved that, the regression combined with BPANN model was sufficient enough in predicting cereals production in Eastern Province of Rwanda
Summary
As reported by Hertz et al (1991), artificial neural networks (ANNs) are used to solve a wide variety of problems in science and engineering, for some areas where the conventional modeling methods fail, and a well-trained ANN can be used as a predictive model for a specific application, which is a dataprocessing system inspired by biological neural system.The neurons can be classified into three types: input, output and hidden; input neurons are the ones that receive input from the environment, output neurons are those that send the signals out of the system and neurons which have inputs and outputs within the system are called hidden neurons (Poursina, 2008; Zhou and Kang, 2005). According to (Mrutyunjaya et al, 2011), the input signals are modified by interconnection weight, known as weight factor (wij), which represents the interconnection of ith node of the first layer to jth node of the second layer; the sum of modified signals is modified by the transfer function. In this paper 'tansig' function was used. Output signals of hidden layer are modified by interconnection weight (wij) of kth node of output layer to jth node of hidden layer. The sum of the modified signals is modified by another transfer function. This paper applied ‘purelin’ function and output is collected at output layer
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