A hybrid aco-cs based optimized knn classifier algorithm for rainfall detection & prediction

The detection and prediction of rainfall is an important task in recent years. The usage of machine learning approach in agriculture has enhanced the efficiency of farming. The rainfall detection and prediction will be helpful to farmers to take appropriate actions on sowing, irrigation etc. In this paper, the rainfall detection and classification are done, the investigation of various machine learning approaches like Support Vector Machine, K-Nearest Neighbor (KNN), Decision Tree Neural Networks were done using rainfall data. In this paper we propose a new approach for the optimization of the nearest neighbor numbers in KNN algorithm using a hybrid Ant colony optimization and Cuckoo search algorithm for efficient rainfall detection. The experiments were performed in MATLAB platforms using monthly rainfall data sets that are downloaded from Indian meteorological Department (IMD). Monthly rainfall for years 1901 to 2019 are taken for analysis. The performance of various classification algorithm for rainfall data using the parameters like precision, sensitivity, specificity, and accuracy has been done.