TY - GEN
T1 - Relationships between climatic factors to the paddy yield in the North-Western Province of Sri Lanka
AU - Wickramasinghe, Lasini
AU - Jayasinghe, Jeevani
AU - Rathnayake, Upaka
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/24
Y1 - 2020/9/24
N2 - Climate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.
AB - Climate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.
KW - Artificial Neural Network (ANN)
KW - LM algorithm
KW - North-Western province
KW - Paddy yield
KW - Rainfall
KW - Temperature
UR - http://www.scopus.com/inward/record.url?scp=85100597685&partnerID=8YFLogxK
U2 - 10.1109/SCSE49731.2020.9313047
DO - 10.1109/SCSE49731.2020.9313047
M3 - Conference contribution
AN - SCOPUS:85100597685
T3 - Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2020
SP - 223
EP - 227
BT - Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Research Conference on Smart Computing and Systems Engineering, SCSE 2020
Y2 - 24 September 2020
ER -
