Climate change forecasting using data mining algorithms

Abstract

Water management is very important for human life sustainability. Rainfall forecasting is one of the most important factors for the water management of an area. A forecast is simply a calculation of what happens in the future based on past information under the assumption that the pattern followed in the past would continue in the future also. This work aims at obtaining forecasting models for the time series data set using conventional models and computational models. Varanasi City's annual climate data for a total of 113 years is used for the analysis. Initially, the individual model is considered and used for forecasting. Later, hybrid models will be considered and a comparison between individual models and hybrid models would be obtained. The individual statistical models to be considered are moving average, exponential smoothing with one parameter, and autoregressive integrated moving average (ARIMA). The forecast is also done individually using the k-nearest neighbor (kNN) and interpolation technique cubic spline. Finally, the best-chosen statistical models and the interpolation model are coupled with kNN to develop hybrid models and with these hybrid models, the forecast is done for the data. All the models will be compared and the best among them will be chosen. HIGHLIGHT Rainfall forecasting is very important for water management, here we have compared the five latest AI/data science techniques of forecasting.; Study area is Varanasi, the oldest city in India.; 113 years of climate data is used.; Models used are ARIMA, kNN, spline, exponential smoothing.; The hybrid model was prepared for better forecasting.;

Keywords

• arima
• data mining
• forecasting
• knn
• water management
• weather data