Main Reference PaperShort-term Rainfall Forecasting Using Multi-layer Perceptron, IEEE Transactions on Big Data, 2018 [Python/Hadoop]
  • Rainfall forecasting is mainly affected by the following factors, such as high-altitude physical factors and surface factors. To improve the overall forecasting accuracy, this work proposes a named Dynamic Regional Combined short-term rainfall Forecasting approach (DRCF) using Multi-layer Perceptron (MLP). In data preprocessing, Principle Component Analysis (PCA) is used to reduce the dimension and determine the exact input of MLP. A special greedy algorithm and Stochastic gradient descent (SGD) with momentum are adopted to determine the suitable network structure and parameters of MLP.

Description
  • Rainfall forecasting is mainly affected by the following factors, such as high-altitude physical factors and surface factors. To improve the overall forecasting accuracy, this work proposes a named Dynamic Regional Combined short-term rainfall Forecasting approach (DRCF) using Multi-layer Perceptron (MLP). In data preprocessing, Principle Component Analysis (PCA) is used to reduce the dimension and determine the exact input of MLP. A special greedy algorithm and Stochastic gradient descent (SGD) with momentum are adopted to determine the suitable network structure and parameters of MLP.

  • To improve the prediction accuracy for rainfall forecasting.

  • To reduce the dimensionality of data.

Aim & Objectives
  • To improve the prediction accuracy for rainfall forecasting.

  • To reduce the dimensionality of data.

  • In the proposed work, the rainfall forecasting does not handle the missing data, it decreases the classification accuracy. So the algorithm is contributed for missing data prediction.

Contribution
  • In the proposed work, the rainfall forecasting does not handle the missing data, it decreases the classification accuracy. So the algorithm is contributed for missing data prediction.

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