This study presents an overview of seismic hazards in Bhutan and the vulnerability of Bhutanese vernacular constructions derived from damage caused by two earthquakes that occurred in 2009 and 2011. Interpretations of damage and seismically vulnerable features of such constructions are provided from field observations. The taxonomy and vulnerability of all existing Bhutanese buildings are presented using a newly developed classification system. Empirical seismic fragility functions for vernacular stone masonry buildings are constructed using the damage data of 2009 and 2011 earthquakes. The sum of observations highlights that Bhutanese masonry buildings are highly vulnerable even in moderate far-field earthquakes.

Transport networks are frequently exposed to geohazards, and losses can be observed frequently by notable disaster incidents such as landslides, earthquakes, and floods. Although losses due to natural disasters are frequently reported, limited attention is paid to assess the condition of strategic road networks in the Himalayas. Thus, to fulfill this gap, this study performs condition assessment of Asian Highway AH-48 in Bhutan and develops a system to rate the performance level of road assets. The assessment is conducted along a 157.6 km stretch of AH-48. The sum of findings highlights that about 55% of the road assets along the AH-48 are highly vulnerable to geohazards. Drains are observed to be the most vulnerable, with 52% drains lying below the standard rating among the considered assets followed by retaining walls and pavement. Landslide vulnerability function for highways that are situated in mountain regions is developed using the newly proposed landslide intensity scale. The sum of findings highlights that the Himalayan roads will be affected mostly by landslides and the drainage system is the most affected road asset showing the least serviceability among the common road assets.

Multifarious anthropogenic activities triggered by rapid urbanization has led to contamination of water sources at unprecedented rate, with less surveillance, investigation and mitigation. The use of artificial intelligence (AI) in tracking and predicting water quality parameters has surpassed the use of other conventional methods. This study presents the assessment of three main models: adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) on water quality parameters of Wangchu river located at capital city of Bhutan. The performance and predictive ability of these models are compared and the optimal model for predicting the parameters are recommended based on the coefficient correlation (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE) evaluation criteria. Overall NSE and RMSE, the ANN model predicted parameters with maximum efficiency of 97.3 percent and minimum error of 8.57. The efficiency of MLR and ANFIS model are 95.9 percent and 94.1 percent respectively. The overall error generated by MLR and ANFIS are 10.64 and 12.693 respectively. From the analysis made, the ANN is recommended as the most suitable model in predicting the water quality parameters of Wangchu river. From the six-training function of ANN, trainBR (Bayesian Regularization) achieved the CC of 99.8%, NSE of 99.3% and RMSE of 9.822 for next year data prediction. For next location prediction, trainBR achieved CC of 99.2%, NSE of 98.4% and RMSE of 6.485, which is the higher correlation and maximum efficiency with less error compared to rest of the training functions. The study represents first attempt in assessing water quality using AI technology in Bhutan and the results showed a positive conclusion that the traditional means of experiments to check the quality of river water can be substituted with this reliable and realistic data driven water models. Article highlights: Total dissolved solids (TDS), electrical conductivity (EC), potential of hydrogen (pH) and dissolved oxygen (DO) are selected as main water quality parameters as data for modeling. Artificial neural network model gives highest efficiency and accuracy compared to MLR and ANFIS model. Use of artificial intelligence shows better performance to provide water quality and future predictions over conventional methods leading to conservation of water resources and sustainability.

Windstorms can cause significant damage to non-engineered structure and infrastructure due to the lack of consideration of wind force in design and construction. Historical windstorm events reflect that the windstorm damage is different than most of the other dynamic forces such as earthquakes, floods, tsunamis, and landslides. Bhutan frequently observes major windstorm events that cause significant damage to structures and infrastructures. Between April 2008 and May 2021, 51 windstorm events were recorded in Bhutan. Owing to widespread damages and losses in structures and infrastructures, it is imperative to depict the windstorm vulnerability of structures and infrastructures that would be instrumental, especially in a country such as Bhutan where windstorms are frequent and enormous losses are attributed to damage to structures and infrastructures. Based on historical windstorm observations and database, we present forensic interpretations of damage mechanisms and windstorm vulnerability analysis. Apart from this, we also present some strengthening techniques considering inherent vulnerabilities, especially for vernacular constructions in rural Bhutan.