Artificial Intelligence to the Assessment, Monitoring, and Forecasting of Drought in Developing Countries
G. Manikandan, G Bhuvaneswari, M. Robinson Joel
Abstract
In order for plants to respond to specific degrees of moisture stress that affect both vegetative development and crop production, circumstances called “drought” must exist. It happens when the amount of moisture that can be held in the soil to suit a specific crop's needs is insufficient. India's drought has two main causes: climate change and a lack of surface water supplies. In some cases, it may be able to pinpoint the direct cause of a drought in a specific area, but this is not always the case. Consequently, it is imperative to establish an effective method for communicating the Standardized Precipitation Index SPI data revealing drought indices to farmers and strengthen drought and climate resilience in order to improve all these services in favour of improving agricultural productivity and decreasing food insecurity in India. Understanding past drought experiences with precise indicators is essential to developing future plans and policies in India's agriculture industry. Since this study would aid in India's agricultural development, it is obvious that a standardised drought index must be used to comprehend how frequently droughts are occurring across the country. The major goal of this study is to establish a suitable baseline for drought index forecasting using Standardized Precipitation Index SPI data. As a result, the project's ultimate result would be a knowledge base from which appropriate forecasting tools and distribution networks for farmers might be updated or established. Also, experiment with the logistic regression algorithm to get the best prediction.