Call for Paper : Interdisciplinary: Application of AI, ML and NIOT in Trend Analysis and Short-Term Forecasting of Extreme Events
Special Issue in Applied Water Science Springer Scopus indexed
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Short-term forecasting of extreme events is a method that predicts the occurrence of extreme events at an interval of 3 to 5 days, helping authorities and communities prepare for potential impacts of such events. Accurate forecasting is crucial for minimizing the impact of extreme events on communities, infrastructure, and the environment, enabling timely evacuation and emergency response efforts. It also helps in determining where to allocate resources and implement preventative measures to mitigate the vulnerabilities.
Artificial Intelligence (AI), Machine Learning (ML) and Nature-Inspired Optimization Techniques (NIOT) have shown great potential in improving the accuracy and timeliness of short-term or very short-term forecasting through data analysis and pattern recognition. These technologies can process large amounts of data quickly, allowing for more precise predictions and better decision-making during emergency situations. By utilizing AI and ML, emergency response teams can better anticipate the impact of uncertainties caused by extreme events and take proactive measures to mitigate risks. As research in this field continues to evolve, more innovative solutions will emerge to help communities better prepare for and respond to such events.
While AI and ML offer numerous benefits, they also have limitations, such as reliance on historical data and the complexity of these algorithms. Despite these limitations, the integration of AI and ML into flood, drought, or storm management systems has the potential to save lives and reduce the economic impact of climatic or anthropogenic extremities.
This Collection supports and amplifies research related to SDG 11, SDG 13, and SDG 15.
Keywords: extreme events, uncertainty analysis, climate change impacts, short term forecasting, real time monitoring, artificial intelligence, big data analysis, polynomial neural network, group method of data handling, multiple-criteria decision analysis, machine learning, clusterization, unsupervised classification, image processing, data segmentation, pattern recognition
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