Climate change, rainfall, weather forecasting is of great concern during the past two decades as scientists and researchers are cautious in building standard numerical models to simulate and forecast the weather parameters in efficient and reliable way. In India, the monsoon is largely responsible for rainfall. India experiences three distinct seasons throughout the year as a result of the monsoon, which originates from the reversal of the predominant wind direction from Southwest to Northeast. Between June and October, the Southwest monsoon, sometimes known as the “wet” season, brings significant rainfall across the majority of the nation. The focus of this research work is to analyse the data of rainfall existed in the past 100 years (1901–2000) and implementing artificial intelligent methods to frame certain classification of algorithm which can forecast the level of rainfall in the future. Data from 1901–2000 of Chennai district has been taken into account for this research. Statistical evaluations are done based on the database and the tabulated results shows the significance of rainfall. Wavelet analysis of multi resolution criteria is obtained to extract the information of heavy rainfall. Mann Kendall (MK) test statistics is utilized for classifying the rainfall data in four levels viz., very-low, low, moderate, high and very high. Trend analysis for the 17 years is tested using Neuro Fuzzy optimisation algorithm. The efficient training of Neuro fuzzy algorithm forecasts the possible trend using the classification analysis of MK test.

Geoelectrical resistivity data collected from the ground contain lot of noises and errors. It requires efficient algorithm to reduce the errors to make an actual inversion models. Though different algorithm can be applied, nature inspired algorithm is more potential in inverting geoelectrical data in an elegant and comprehensive way. Bargain Optimization (BO) algorithm is framed on the concept of bargaining things to purchase for needs. In general, effective bargaining results in more profit and leads to loss when it fails. In this research work, Bargain Optimization algorithm is applied to invert geoelectrical data and the effective bargaining will take time to process and to obtain the required model. The input data is AB/2, apparent resistivity data and the inverted model through BO algorithm is successfully matched with the available litholog section of the study area. The output graphs have profit/loss bar graph, which reveals the status of bargaining during a particular number of epochs.