## 5 Conclusions  
The present study proposed an RNN model for earthquake probability mapping in Odisha, India. The use of artificial intelligence and GIS in earthquake studies adds a new, beneficial dimension. Therefore, this work developed a SSA and EPM map based on the RNN classification technique, using direct attributes of pertinent criteria. This study demonstrated that the spatial variation of seismic amplification was very high in coastal parts of Odisha and high in the Mahanadi basin. Similarly, the probability map shows that very high-to-high probable locations for future events could be found in the western part of the state. The probability results denoted that structural attributes and complex geological/geomorphological features in western NOBF could be the main reason for high probability. However, a noticeable intersection of faults in the shear zone could have control on isoseismic elongation in the west. The study also shows the Mahanadi graben spreading and creating a huge basin for recent deposits. Hence, the active graben can damage the cities when originate from NOBF and the Bay of Bengal faults.  
The RNN model was applied to transfer areas with similar or more complex tectonics and had several limitations. The proposed model and data analysis can cover a large study area but the process is time-consuming. This study can be considered as a reconnaissance study prior to performing a detailed study for mitigation and micro-zonation purposes. The RNN model deals with technical complexities and data uncertainties during the priority analyses. These processes can be modified and improved using integrated and ensemble techniques. The RNN model strictly depends on sequence, quality, and input data preparation.  
Future works can be done in association with geophysical, seismological, geo-structural, geological, and earthquake engineering data, including novel integrated, hybrid, and ensemble techniques. Currently, remote sensing techniques and artificial intelligence techniques are popular and thus there is potential for a future project that uses high-resolution LiDAR techniques and images. In conclusion, the proposed model enables planners, managers, agencies, and policymakers to make decisions for land use planning, as well as scheming appropriate proposals for seismically active areas.  
Conceptualization, R.J. and B.P.; methodology, R.J.; software, B.P.; validation, R.J., B.P.; formal analysis, R.J.; investigation, R.J.; resources, B.P.; writing--original draft preparation, R.J.; writing--review and editing, B.P.; visualization, B.P.; supervision, B.P.; project administration, B.P.; funding acquisition, B.P. and A.M.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Centre for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney. This research was also supported by Researchers Supporting Project number RSP-2020/14, King Saud University, Riyadh, Saudi Arabia.  
**Acknowledgments:** The authors are thankful to the University of Technology Sydney for providing UTS president's scholarship, international research scholarship and UTS TOP-UP scholarship to the first author.  
**Conflicts of Interest:** The authors declare no conflict of interest.