Road traffic accidents are becoming more common, presenting serious public safety and urban development problems. This study employs Geographic Information Systems to conduct a comprehensive spatial and temporal analysis of road traffic accidents, aiming to identify accident hotspots and underlying patterns. By integrating and analyzing accident data from police reports, the distribution and frequency of accidents across different locations and periods were studied. The accident rate has increased by 1.27% in the fiscal year 2020/2021 than 2019/2020, 2.54% in 2021/2022 than 2020/2021, and by 6.03% in 2022/2023 than 2021/2022. About 12.7% of total accidents recorded were minor, 72.4% of total accidents were major and 14.9% of total accidents recorded were observed to be fatal. The findings reveal a high concentration of accidents at the central part along the Araniko highway. Banepa municipality has a high accident rate compared to Dhulikhel and Panauti due to overspeeding, overloading, overtaking, careless driving, mechanical failures, and loss of vehicle control. Minor accidents were frequent at high-traffic intersections, whereas the major and fatal were predominantly concentrated along the Araniko highway. 41% of medium size, 38% of heavy vehicles, and 39% of light vehicles encountered accidents. The study revealed that head-on collisions on the narrow section of Araniko Highway occur often. Intersection management and signalization in Banepa need to be addressed since rear-end collisions increased during periods of high traffic congestion. Two-wheel riders and pedestrians are the more vulnerable. The study utilizes spatial statistical tools such as Kernel Density Estimation (KDE) to identify accident-prone locations. Temporal analysis further explains peak times and seasons for accidents. The findings reveal distinct spatial clusters of accidents and temporal patterns during peak and non-peak hours. This spatial and temporal analysis of accidents showed the need for specific road safety interventions. Through the identification of accident causes and the implementation of recommended measures, these municipalities can significantly lower accident rates and improve overall road safety in their locality.

Hydropower is a significant renewable energy source for Nepal, contributing to its electricity generation and economic development. However, climate change-induced fluctuations in river discharge concerns long-term hydropower sustainability. This study investigates the effects of climate change on the hydropower potential of Nepal’s Sunkoshi basin using a physically based hydrological approach (SWAT) compared with a machine-learning method (Bi-directional LSTM). In the face of increasing global energy consumption and calls for sustainable development, the paper utilizes long historical hydrological records (1980–2015) and future climates in the simulation of river flow for the period of 2024–2050. While the physically realistic data-driven SWAT approach captures physical watershed processes, the BiLSTM exploits the pattern of historical flow for future flow forecast. Both models forecast a nearly 48% reduction in the average flow in the historical period, with significant rises in the duration of low flow with total hydrological variability. Although the correlation coefficient (r = 0.99) for the relationship between the two approaches in predicting the yields of hydropower energy is very high, the results in the forecast of events at extremes conflict: while the SWAT overestimates the peak flows, the BiLSTM offers smoother curves. The paper emphasizes the contribution of multi-model approaches towards hydrological forecasting and highlights the importance of planning for adaptation in the face of changing climatic conditions, for the requirement of adaptation measures, investment in resilient infrastructure, as well as updating policies to maximize the utilization of hydropower in the face of changing climatic conditions. The SWAT model achieved strong calibration performance with R2 = 0.91 and Nash-Sutcliffe Efficiency (NSE) = 0.82, while the BiLSTM model demonstrated superior short-term accuracy with Test MSE = 0.0006, MAE = 0.0099, and RMSE = 0.0246. Energy output projections suggest hydropower generation could decline significantly, emphasizing the need for resilient infrastructure, adaptive policy reforms, and hybrid renewable energy integration. This study underscores the necessity of multi-model approaches for hydrological forecasting and provides critical insights for climate-resilient hydropower planning in Nepal. The findings are instrumental for policymakers, engineers, and researchers aiming to enhance energy security and sustainable development. In this study, the rationale for selecting SWAT lies in its strength to interpret physical watershed processes, while BiLSTM was chosen for its ability to capture short-term temporal dependencies in hydrological time series. The regional significance of this study is emphasized by Nepal’s reliance on hydropower for over 90% of electricity, making accurate discharge forecasting vital for national energy security.