The water quality of Rudrasagar Lake, the second-largest natural reservoir of Tripura is of great ecological and economic importance as it serves a diverse range of purposes, including fishing, irrigation, aquaculture, domestic use, and recreation activities. This study investigates the water quality of the study area, an esteemed Ramsar site in North Eastern India, using a combined application of multivariable statistical and geospatial techniques. In this study, 24 water samples were designed based on their use and collected along the periphery and the inner areas of the lake employing the Latin Square Matrix. This research also examines the spatial variations of water quality involving quartile-based water quality categorization of parameters, with Pearson’s Correlation analysis, Principal Component Analysis (PCA), and Hierarchy Cluster Analysis (HCA) applied for dimension reduction. The analysis involved quartile-based water quality categorization of parameters, with PCA and HCA applied for dimension reduction. Meanwhile, the Inverse distance weighted (IDW) approach was used to interpolate the spatial distribution of the quartile score using the ArcGIS platform. The Bureau of Indian Standards (BIS) was followed for water quality assessment. The results revealed significant spatial variation, providing valuable insights for future water management strategies. PCA indicates 57.26% of the variance in the dataset, whereas samples were classified into three subgroups and two groups in a dendrogram representing the result of the HCA. This study demonstrates the utility of PCA, HCA, and IDW interpolation in water quality assessment, highlighting the effect of human-induced activities in the lake’s vicinity.

Climate disasters have become increasingly frequent in India, severely affecting the railway infrastructure every year. Physical damages to railway tracks, bridges, and signaling systems, caused by floods, cyclones, and landslides, are well documented. However, the impact of these disasters on the railway infrastructure was beyond direct physical damages. This paper aimed to explore the impact of climate disasters on railway infrastructure in Northeast India using case study approach. Three cases were studied to analyse the impact of climate disasters on railway infrastructure, including geological disasters and extreme weather. Infrastructure development and operation of railway transport system in Assam, Mizoram, and Manipur proved to be challenging, especially when coping with natural disasters, such as floods, landslides, and earthquakes. This paper found that disruption of railway services was associated with geo-physical structure of the region, which triggered the disaster vulnerability. The results showed that climate disasters had a significant impact on railway infrastructure in Northeast India in many aspects. Formulation and implementation of strategic policies might reduce the disaster risks. Therefore, policymakers and Ministry of Railways, Government of India should consider this possible probability approach over environmental determinism.

The railway transport system is one of the most important elements in the development of the economy and the social space of any area. The main objective of the study is to analyse the regional differentiation in railway development in Serbia with causal interference. The research has been conducted based on secondary data collected from multiple sources, and the existing synthetic Indicator was applied to classify eight states based on their railway infrastructural status. An alternative synthetic Indicator approach has been proposed and found to be more efficient than the existing synthetic Indicator. The causality of such unequal development has been analysed through a correlation test by defining the composite infrastructure index. The analysis revealed that railway infrastructure significantly influences Serbia's economic and social development. The service area of railway infrastructure indicates the potential zone for future growth.