Trends in Humidity, Rainfall and Groundwater data over Vidarbha Region of Maharashtra using Statistical Analysis

Abstract

The temperature of the atmosphere is growing day by day as a result of global warming and climate change, which is linked to rainfall variability. Understanding the climate system's transition is crucial to tackling major worldwide environmental challenges. Furthermore, because rainfall is the most common cause of stream flow in India, particularly flood flow, it is critical to understand its pattern integrated with groundwater recharge. Seasonally, the amount of rain that falls fluctuates. The volume of rainfall in different sections of a country at different times and locations throughout the year is complicated, and further research is needed. Various hydrological concerns, such as floods and droughts, are caused by this variation. Rainfall research in India during the monsoon season (June to September) and Temperature research in India during the summer season (Feb to May) is important for a variety of reasons, including economic development, disaster management, and hydrological planning. It's vital to understand mean rainfall and temperature variations on a smaller geographical scale. The seasonality index of rainfall and temperature can be used to investigate the changing pattern of rainfall and temperature. Vidharbha region has total 11 district divided into two divisions, Amravati division and Nagpur Division. Total 11 district are akola, Amravati, Buldana, Yavatmal, Washim, Bhandara, Chandrapur, Gadchiroli, Gondia, Nagpur and Wardha. Total Talukas are almost 120 as of July 2021. The main aim of this research is to study a yearly trend for rainfall pattern, humidity and groundwater level using different statistical model. We have also studied the district wise geology and river recharging pattern to understand the relation matrix and co-relation parameter. The observation shows direct dependability of river water flow with rainfall and groundwater recharge with 67% and 78% precision respectively. Humidity dataset shows heavy prediction prospective with 35% accuracy based on the 5-year timeline dataset. We have also verified the data redundancy and data inconsistency for obtaining 99% plus data efficient model. Keywords: Shunt Active Power Filter (SAPF), Intelligent Instantaneous Active and Reactive Power (IPQ) Theory, Hysteresis band current controller (HBCC), Variable Scaling Hybrid Differential Evolution (VSHDE) and Total Harmonic Distortion (THD), Power Quality (PQ).