Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data
| Autor: | Jeevani Jayasinghe, Anushka Perera, Vinushi Amaratunga, Lasini Wickramasinghe, Upaka Rathnayake |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
education.field_of_study
010504 meteorology & atmospheric sciences Mean squared error Artificial neural network Correlation coefficient Article Subject General Mathematics Population General Engineering Climate change Context (language use) 02 engineering and technology Engineering (General). Civil engineering (General) 01 natural sciences Statistics QA1-939 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Climate model Performance indicator TA1-2040 education Mathematics 0105 earth and related environmental sciences |
| Zdroj: | Mathematical Problems in Engineering, Vol 2020 (2020) |
| ISSN: | 1024-123X |
| DOI: | 10.1155/2020/8627824 |
| Popis: | Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text