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A collection of over 250 uses for artificial intelligence

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Earthquake prediction

Introduction

The problem of earthquakes has been one of the most difficult issues for environmental scientists and engineers due to the uncertainty of occurrence. This natural disaster which has the potential of causing devastating losses has not been well captured by the predictive models. However, there has been a recent development in the use of ML and AI for the prediction of earthquakes. These tools, which have the capacity to process large data sets and detect subtle relationships, provide a ray of hope for better and earlier predictions.

Challenges

The problem of earthquake prediction is diverse as well as significant. First, earthquakes are very difficult to predict as they are associated with a large number of factors including seismic activity, geological features and even atmospheric conditions. Also, the information available on earthquakes is immense, diverse and at times inconsistent or contaminated with errors. The conventional predictive models have a challenge in managing such large volume of data with many attributes. Also, since the occurrence of large and destructive earthquakes is rather infrequent it is difficult to test the predictive models. Lastly, the implications of false positive (or false alarm and economic loss) and false negative (consequences of loss of lives and properties due to an earthquake for which preparations have not been made) heightens the importance of earthquake prediction.

AI Solutions

There are several problems that AI, and in particular ML, can address. These technologies are capable of processing large data sets, learning patterns and making forecasts. One of the most advanced forms of ML, which is the deep learning, is especially effective when dealing with high-density data and can help to recreate the relationships between various factors involved in an earthquake. In addition, ML models can be trained with the help of previous earthquake data to predict the next seismic events. For instance, TensorFlow, an powerful and popular open-source library for ML developed by Google, has been applied in the creation of models that are capable of forecasting earthquake aftershocks. Yet another creative method is the employment of CNNs in the interpretation of seismograms and identification of the initial characteristics of an earthquake.

Benefits

There are several ways in which AI and ML can be useful in enhancing the capabilities of predicting earthquakes. First, it can enhance the efficiency and reliability of the predictions, which can help in preventing loss of life and property. Second, it can also help in the analysis of the seismic data that would otherwise require a lot of time from scientists. Third, it can assist in recognizing of new patterns and trends in the earthquake data, increasing the knowledge about such events. Lastly, they can assist in improving the accuracy of the predictions to enable governments and other organizations to improve on how they manage disasters through preparedness and response.

Return on Investment

The ROI of AI and ML in earthquake prediction can be very high. Although it is quite hard to express the returns in monetary terms since the gains are intangible and delayed, the potential savings from preventing the damage and fatalities can be very high. A research done by the National Institute of Building Sciences indicated that every dollar invested in hazard mitigation, including earthquake prediction saves six dollars on future disaster costs. Also, the application of these technologies results in the enhancement of the utilization of resources hence increasing the ROI.

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