Publications

Ahmed, M., Tanyas, H., Huser, R., Dahal, A., Titti, G., Borgatti, L., Francioni, M., & Lombardo, L. (2023). Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system . Earth ArXiv. https://doi.org/10.31223/X5JT2D

Dahal, A. , & Lombardo, L. (2023). Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling. Computers & geosciences , 176, [105364]. https://doi.org/10.1016/j.cageo.2023.105364

Wang, T. , Dahal, A., Fang, Z., van Westen, C., Yin, K., & Lombardo, L. (2023). From space-time landslide susceptibility to landslide risk forecast . Earth ArXiv. https://doi.org/10.31223/X5XT1F

Elia, L., Castellaro, S., Dahal, A. , & Lombardo, L. (2023). Assessing multi-hazard susceptibility to cryospheric hazards: lesson learnt from an Alaskan example . Earth ArXiv. https://doi.org/10.31223/X5RQ27

Wang, N., Zhang, H., Dahal, A., Cheng, W., Zhao, M., & Lombardo, L. (2023). On the use of explainable AI for susceptibility modeling: examining the spatial pattern of SHAP values . Earth ArXiv. https://doi.org/10.31223/X5P078

Mishra, B., Bhandari, R., Bhandari, K. P., Bhandari, D. M., Luintel, N., Dahal, A., & Poudel, S. (2023). High-resolution mapping of seasonal crop pattern using sentinel imagery in mountainous region of Nepal: A semi-automatic approach. Geomatics , 3(2), 312-327. https://doi.org/10.3390/geomatics3020017

Tanyas, H. , He, K. , Sadhasivam, N. , Lombardo, L. , Chang, L., Fang, Z., Dahal, A. , Fadel, I., Hu, X., & Luo, G. (2023). Monitoring and prediction of InSAR-derived post-seismic hillslope deformation rates . Abstract from EGU General Assembly 2023, Vienna, Austria. https://doi.org/10.5194/egusphere-egu23-14415

Dahal, A. , Tanyas, H. , van Westen, C. , van der Meijde, M., Mai, P. M., Huser, R., & Lombardo, L. (2023). Space-time modelling of co-seismic and post-seismic landslide hazard via Ensemble Neural Networks. . Abstract from EGU General Assembly 2023, Vienna, Austria. https://doi.org/10.5194/egusphere-egu23-3496

Dahal, A., Cruz, D. A. C., Tanyas, H. , Fadel, I., Mai, P. M., Meijde, M. V. D. , Westen, C. V., Huser, R., & Lombardo, L. (2023). From ground motion simulations to landslide occurrence prediction . Earth ArXiv. https://doi.org/10.31223/X5WM0P

He, K. , Lombardo, L. , Chang, L. , Sadhasivam, N., Hu, X., Fang, Z. , Dahal, A. , Fadel, I., Luo, G., & Tanyas, H. (2022). Hillslope recovery after a major earthquake: InSAR-derived time series analyses to capture earthquake-legacy effect . https://doi.org/10.31223/X5Q65W

Dahal, A. , van den Bout, B. , van Westen, C., & Nolde, M. (2022). Deep Learning-Based Super-Resolution of Digital Elevation Models in Data Poor Regions. Earth ArXiv. https://doi.org/10.31223/X5DD21

Dahal, A. , & Lombardo, L. (2022). Explainable artificial intelligence in geoscience: a glimpse into the future of landslide susceptibility modeling . Earth and Space Science Open Archive. https://doi.org/10.1002/essoar.10512130.1

Dahal, A. , Tanyas, H. , van Westen, C. , van der Meijde, M., Mai, P. M., Huser, R., & Lombardo, L. (2022). Space-time landslide hazard modeling via ensemble neural networks . Earth ArXiv. https://doi.org/10.31223/X5B075

Dahal, A. , van den Bout, B. , van Westen, C. J., & Nolde, M. (2022). Spatial Loss Function for Super-Resolution of Geoscientific Data . Paper presented at Living Planet Symposium, LPS 2022, Bonn, North Rhine-Westphalia, Germany.

Mishra, B., Dahal, A., Luintel, N., Shahi, T. B., Panthi, S., Pariyar, S., & Ghimire, B. R. (2022). Methods in the spatial deep learning: current status and future direction. Spatial Information Research , 30(2), 215-232. https://doi.org/10.1007/s41324-021-00425-2

van Westen, C., Hazarika, M. K., Dahal, A., Kshetri, T., Shakya, A., & Nashrrullah, S. (2022). The RiskChanges tool for multi-hazard risk-informed planning at local government level . Abstract from EGU General Assembly 2022, Vienna, Austria. https://doi.org/10.5194/egusphere-egu22-3026

Dahal, A., Sharma, P., & Hazarika, M. K. (2020). Implementation of integrated geospatial platform, database, and application for disaster risk management in Uttarakhand . 1-10. Paper presented at 40th Asian Conference on Remote Sensing, ACRS 2019 , Daejeon, Korea, Republic of.

Dan, T. T., Hazarika, M. K., Miyazaki, H., Nashrrullah, S., Dahal, A., & Shibasaki, R. (2018). Estimation of population distribution using satellite imagery and GIS data . 539-547. Paper presented at 39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018, Kuala Lumpur, Malaysia.