| Abstrakt: |
In current studies, India is a farming country, and the accomplishment or disappointment of the crop mainly depends on the country's rainfall design. Generally, India's farming production is primarily based on the nature of the precipitation of the rainy season rainfall. The rainy season is the primary source of water in India. Regular rainfall forecasting is the primary source for crop development. Several analyses have defined the direct effect of rainwater on harvests. The main motive of this research work is proper and early rainfall prediction, which is helpful to people who live in northeast regions inclined to natural disasters like floods, etc. It helps agriculture with decision-making in their crop and water management (WM) using extensive dataset analysis that generates maximum terms of production for farmers and profits. This proposed work introduced an improved rainfall forecasting framework, a hybrid feature selection gradient-based RNN (HFSGRNN) model with an RNN algorithm. The research uses the HFSGRNN model steps, such as initial data preprocessing steps, which are used for forecasting rainfall, handling missing value outliers, and typecasting the rainfall dataset collected from the government site. After that, an HFSGRNN method is implemented to select the valuable using stochastic gradient descent (SGD) and optimal solutions calculated by particle swarm optimization (PSO) from the preprocessed data. The hybrid optimized feature sets are fed to the rainfall forecasting of the RNN classifier. Lastly, the valuable feature sets are forecasted using decision-making, and the simulation outcome shows that the research approach performed better in rainfall forecasting. The simulation results define that the HFSGRNN model delivered the minimum value of Root means square error (RMSE= 0.10) and maximum value of accuracy rate (acc = 98.1%) compared with existing methods, such as logistic regression (LR), Long Short Term -Convolutional Neural Network (LSTM-CNN), etc. The outcomes of the research analysis will help the farmers accept efficient modeling methods for forecasting long-term seasonal rainfall. [ABSTRACT FROM AUTHOR] |
