Estimation of Monthly Rainfall using Machine Learning Approaches

Hydro-meteorological measures are one of the most challenging tasks of nature. Rainfall has become the most significant and technical factor for scheduling and functional criteria of any irrigation program with profound effects on agribusiness based economy for any region. It is vital important to accurately estimate rainfall and has been remained a great challenge. To estimate medium range monthly summer monsoon rainfall, machine learning models were developed using thirteen rain-gauges' long-term-average rainfall values of Jaipur city. The different input combinations of rain-gauges having 2, 4, 6, 8, 10 and all-input parameters were explored using rainfall values of rain-gauge stations of Viratnagar, Bassi, Chaksu, Chomu, Jaipur, Dudu, Jamuna_Ramgarh, Kotputli, Phagi, Phulera, Amber, Sanganer, Shapura in Jaipur city. Exploring various empirical and dynamic machine learning approaches on rainfall data of 54 years period [1961-2015], rain-gauge station-wise were computed to evaluate the best rain-gauge station of Jaipur city with selection of best model for medium range estimation of monthly summer monsoon rainfall of Jaipur city. In the course of escalated experimentation, it is observed that Linear Regression outperforms over rest of other machine learning techniques and Amber rain-gauge station is the best rain-gauge among all used stations. The dataset utilized for this work is accessible as supplement at http://waterresources.rajasthan.gov.in authored by Government of Rajasthan.