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Evolving and evaluation of 3LP FFBP deterministic ANN model for district level long range monsoon rainfall prediction
S Karmakar1, M K Kowar, P Guhathakurta
1Bhilai Institute of Technology, Bhilai House, Durg.491001, Chhattisgarh. karmakar_sanjeev@yahoo.com
Journal of Environmental Science & Engineering
|December 1, 2010
Summary
This study introduces Three Layer Perception Feed Forward Back Propagation Deterministic Artificial Neural Network models for monsoon rainfall forecasting in Chhattisgarh. These models demonstrate excellent performance, outperforming traditional statistical methods.
Area of Science:
- Meteorology
- Artificial Intelligence
- Data Science
Background:
- Accurate monsoon rainfall forecasting is crucial for agriculture and water resource management in Chhattisgarh.
- Traditional statistical models often struggle with the complex, non-linear patterns of monsoon rainfall.
Purpose of the Study:
- To develop and evaluate Artificial Neural Network (ANN) models for predicting monsoon rainfall in Chhattisgarh districts.
- To assess the performance of Three Layer Perception Feed Forward Back Propagation (3LP FFBP) models against statistical trend models.
Main Methods:
- Proposed 3LP FFBP Deterministic ANN models with specific configurations (11 input neurons, 11 hidden neurons, 1 output neuron, sigmoid function).
- Network training utilized data from 1945-1995, with validation using data from 1996-2006.
- Mean Squared Error (MSE) was minimized to below 0.001 for network training.
Main Results:
- ANN models achieved low mean absolute deviation, less than half the standard deviation for the validation period (1996-2006).
- The 3LP FFBP Deterministic ANN models demonstrated superior performance compared to statistical trend models.
- The developed models proved effective for monsoon rainfall forecasting across all districts.
Conclusions:
- 3LP FFBP Deterministic ANN models offer a highly accurate and reliable approach for monsoon rainfall forecasting in Chhattisgarh.
- The study highlights the potential of ANNs in improving meteorological predictions for regional climate challenges.
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