Cyclone track forecasting based on satellite images using artificial neural networks

Cyclone track forecasting based on satellite images using artificial neural networks

Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone attacks. To mitigate the damages caused by cyclones, improved cyclone forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAAAVHRR satellite images. A...

A Review of Epidemic Forecasting Using Artificial Neural Networks

Background and aims: Since accurate forecasts help inform decisions for preventive health-careintervention and epidemic control, this goal can only be achieved by making use of appropriatetechniques and methodologies. As much as forecast precision is important, methods and modelselection procedures are critical to forecast precision. This study aimed at providing an overview o...

Utilizing satellite images and artificial neural networks in estimation of vegetation fraction in arid regions

An Approach of Artificial Neural Networks Modeling Based on Fuzzy Regression for Forecasting Purposes

In this paper, a new approach of modeling for Artificial Neural Networks (ANNs) models based on the concepts of fuzzy regression is proposed. For this purpose, we reformulated ANN model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ANN models. Hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility for forecas...

River Flow Forecasting Using Artificial Neural Networks

River flow forecasting is required to provide basic information on a wide range of problems related to the design and operation of river systems. The availability of extended records of rainfall and other climatic data, which could be used to obtain stream flow data, initiated the practice of rainfall-runoff modelling. While conceptual or physically-based models are of importance in the underst...