Intelligent Methods Used for Obtaining Weather Derivatives: A Review

Gujanatti Rudrappa, Nataraj A. Vijapur

Engineering and Applied Sciences · 01 January 2019

In one sentence. Survey of machine learning for weather parameter estimation tied to weather derivatives, with a proposed image-processing and neural network method.

Abstract

Weather is the condition of the atmosphere and forecasting is predicting the condition of the atmosphere in near future. Weather forecasting is a formidable challenge as weather is a multi- dimensional, continuous and chaotic process. The distinct nature of the model forecasting in all situations accurately is challenging. Presently weather conditions are being obtained from satellites, Doppler radar, radio sounds, observations from aircraft and ground. Collected data is subjected to various statistical and machine learning techniques. These techniques can incorporate relatively simple observation of the sky to highly complex computerized mathematical models. Weather forecasting still remains a challenging issue, due to unpredictable and chaotic nature of weather. Even with present methods the weather forecasting system may still fail to predict weather attribute, therefore there is still scope left to improve these systems. The objective of carrying out the survey is to look forward on how machine learning can help us to improve weather parameter estimation. In this paper we report the different methods carried out by leading researchers, formidable challenges and present our views on development of efficient weather forecasting system. Also, we propose a method which makes use image processing and neural networks to achieve the weather parameter estimation.

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