Article’s

Problems Discussed : 1. Lack of modern and dependable systems in place for alerting people about earthquakes Challenge: A major issue with using AI to set up an earthquake alert system is that the existing technology and data available in Nepal aren’t sufficient for predicting earthquakes in the Eurasian plate That depends on traditional instruments which are not efficient because of lack of information and prediction of seismic events. Nepal which is located at a major fault line, faces a danger due to this issue. Consequences: Many regions of Nepal, Northern India, and Tibet remain in great danger and have a small response time to respond due to the lack of EAS and the use of traditional methods. Previously a 7.8 magnitude earthquake in 2015 left Nepal and some parts of India devastated due to lack of EAS and unreliable technology. 2. Lack of use of AI models in the EAS system Challenge: The main challenge in using AI models for earthquake prediction in Nepal is the lack of information and awareness regarding their use in earthquake detection and prediction on a national level. The use of AI allows real-time data analysis, improving earlier predictions and preparations for seismic activity and aftershocks. Consequences: Whenever earthquake prediction depends on inefficient and generic data, it results in slow detection of seismic events, and missing analysis of complex seismic patterns for reliable prediction due to which only generalized data is available which is insufficient in determining earthquake 3. Lack of awareness and information in remote areas of Nepal Challenge: Communities in remote areas of Nepal have not had any access to knowledge and training around seismic events or the round use of AI or technology to predict earthquakes. As a result following an earthquake in remote areas, the responses are slow and not always effective Consequences: This lack of education and training causes injuries, loss of lives, property damage, displacement, loss of resources, and worsening situations in remote areas where Search and Rescue operations are hard to conduct. 4. Lack of Government Funding and Support Challenge: The Nepalese government with a lack of manpower and technology, hasn’t developed policies and strategies for earthquake prediction for the Eurasian plate. The lack of research and development at the higher level results in inefficient planning at all levels of the Nepali government Consequences: The lack of policy formation and guidance has led to incompetence, delaying meaningful progress in a national policy for earthquake prediction that could be used efficiently. Solution: AI can be used for the prediction of earthquakes and the design of Early Warning Systems (EAS)by using Machine Learning (ML) and Deep Learning (DL) techniques, improving the accuracy and efficiency of the system, hence enabling timely predictions. Some of the methods include: 1. Artificial Neural Networks (ANN) This system is a common type of machine learning(ML)tool in earthquake detection and prediction. ANNs are mainly good at detecting tricky patterns and nonlinear relationships in seismic data.ANNs detect nonobvious relations between data that are missed by other systems 2. Convolutional Neural Networks (CNN) The use of this neural network is detecting seismic waves through image and object recognition, it can be used to find variances of depth, and magnitude aftershocks it recognizes seismic patterns that other models have missed. 3. Long Short Term Memory Networks (LSTM) This model can learn from past earthquakes and predict the location, time, and magnitude of an incoming earthquake from past data.