Earthquake-induced landslide susceptibility zonation (EQ-LSZ) mapping commonly relies on the landslide inventory. However, in many seismically active regions, the lack of comprehensive landslide inventories poses challenges for susceptibility mapping. This study focuses on the Indian Himalayan region, specifically Sikkim, which experienced a series of earthquake-induced landslides, including those triggered by the 2011 Sikkim earthquake. The research develops EQ-LSZ maps using both inventory-inclusive methods—statistical models (Frequency Ratio, FR) and machine learning models (Random Forest, RF)—and an inventory-exclusion method (Newmark Displacement, ND). The study also performs a comparative analysis of these models. Input data include landslide inventory, seismic parameters (peak ground acceleration), landslide-controlling factors (topography, lithology, distance to faults, hydrology, distance to roads, land use/land cover, geomorphology, soil properties), factor of safety, and yield acceleration. Model performance was evaluated using success and prediction rate curves. The FR method achieved 85.34
To develop management strategies to control human-induced air pollutants, it is important to examine the chemical species that influence PM2.5 concentrations and to quantify PM2.5 concentrations in urban environments. The present study examined the 24h average PM2.5 levels from July 2018 to December 2019 at three locations in the Indian Himalayan Region: Darjeeling, Almora, and Mohal-Kullu. During the measurement period, the average mass concentrations of PM2.5 were as follows: Mohal-Kullu, 39 ± 21 (range: 13–98 µg m−3); Almora, 27 ± 18 (range: 11–109 µg m−3); and Darjeeling, 38 ± 13 µg m−3 (range: 16–89 µg m−3). A significant positive correlation among NH4+, SO42−, NO3−, and Cl− in PM2.5 at the p < 0.05 significance level indicated formation of secondary inorganic aerosols (SIA) at the study sites. PMF 5.0 identified three to five major sources over the study sites: fossil fuel combustion, secondary aerosols, vehicular emissions, soil dust, and biomass burning. In Darjeeling, the five dominant sources were coal combustion (26
A paradigm shift is occurring in the manufacturing sector globally due to the digitization of operations; however, studies specific to the maintenance aspect are scarce. The research presents challenges particular to the implementation of smart maintenance in Indian manufacturing SMEs, which is unique and helps bridge the gap. This research aims to identify and analyze the hierarchical interrelationship among critical barriers to smart maintenance implementation in Indian manufacturing Small to Medium-sized Enterprises (SMEs). This study involves a two-phase approach. In the first phase, the critical barriers of smart maintenance in Indian manufacturing SMEs are identified. A literature review, along with the opinions of Industrial and academic experts, resulted in the finalization of the barriers. In the second phase, the Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) methodologies were employed to analyze the criticality of the barriers. The driving dependence power was computed for each factor involved, which was then given as an input into the MICMAC analysis to cluster the barriers into autonomous, dependent, linkage, and independent barriers. Smart maintenance is gaining traction worldwide in the manufacturing sector, driven by the emergence and popularity of Industry 4.0. The study identifies the critical hindrances to the adoption of innovative practices specific to the maintenance function of Indian SMEs. ‘Limited Interoperability of Digital Systems’ and ‘Poor Data Quality and Fragmented Data Infrastructure’ emerged as the critical barriers with high driving power. At the same time, ‘Management Resistance and Low Strategic Priority’ and ‘Lack of Cross-functional Collaboration’ appear at the top of the ISM model with high dependence power. The overall approach of smart maintenance, as presented in this research, challenges identification and structured modelling, and is expected to help managers and policymakers concentrate their efforts based on the criticality of barriers. In turn, this will facilitate a smooth transition to smart technologies in the maintenance function of Indian manufacturing SMEs.
Earthquake-induced landslides (EQILs) pose a major hazard in seismically active mountain regions; however, the relative influence of different strong ground-motion intensity measures (IMs) on landslide susceptibility remains poorly constrained. This study evaluates the role of key IMs in controlling earthquake-induced landslide susceptibility (EQ-LS) in the Sikkim Himalaya, India, using the 2011 Sikkim earthquake (Mw 6.9) as a case study. For this, a comprehensive inventory of 1,120 coseismic landslides was first compiled within the epicentral region. Seismic, topographic, and hydrological conditioning factors were integrated to develop EQ-LS models using six machine-learning algorithms. Strong ground-motion intensity measures (IMs), Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), and Arias Intensity (Ia) were then tested individually and in combination to assess their influence on model performance. Among the evaluated models, the Extremely Randomized Trees (ET) achieved the highest predictive accuracy (0.84) and area under the receiver operating characteristic curve (AUC = 0.92). Models incorporating combined IMs consistently outperformed those based on single parameters, with the PGV–Ia combination yielding the best performance (AUC = 0.93). Feature importance analysis using SHapley Additive exPlanations (SHAP) indicates that velocity-based ground-motion metrics exert a dominant control on landslide initiation. These findings underscore the critical role of PGV in governing EQ-LS. The proposed machine-learning framework offers a robust and transferable approach for EQ-LS mapping in seismically active mountainous regions, with direct implications for improved landslide hazard assessment and disaster-risk reduction planning. This graphical abstract illustrates a machine-learning (ML)–based framework for assessing earthquake-induced landslide susceptibility (EQ-LS) in the Sikkim Himalaya, using the 2011 Mw 6.9 Sikkim earthquake as a case study. A comprehensive inventory of 1,120 coseismic landslides was analysed together with key landslide-conditioning factors, including topographic, geological, hydrological, and seismic variables. Six ML algorithms—Random Forest (RF), XGBoost (XGB), Extremely Randomized Trees (ET), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Linear Regression (LR) were initially trained using Peak Ground Acceleration (PGA) as the sole seismic input. The dataset was partitioned into 70
Understanding the non-stationary behavior and temporal dependence properties of rainfall characteristics, along with their associations with the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), is crucial for the planning and management of agriculture and water resources sectors in Thailand. The study employed the Mann–Kendall test and the Mann–Whitney–Pettitt test to identify monotonic trends and abrupt shifts in rainfall characteristics in Thailand during 1951–2023, respectively. Short-term and long-term persistence were quantified using the autocorrelation coefficient and the Hurst exponent (H), respectively, while correlation analysis examined the association between rainfall characteristics and ENSO/IOD. Results indicated that a small proportion of meteorological rainfall observations exhibit significant trends, shifts, and short-term persistence. However, significant long-term persistence is more prevalent, particularly for indices describing moderate-intensity rainfall and wet-day frequency. Further, ENSO exhibits significant associations with both seasonal and extreme rainfall, with the strength and spatial extent of these associations varying across timescales, regions, and rainfall indicators. The warm phase of ENSO is generally associated with reduced rainfall, fewer rainy days, shorter wet spells, and longer dry spells, while the cold phase exhibits the opposite pattern. The linkage between IOD and rainfall characteristics is generally weak, inconsistent, and confined to specific regions and indices, yet still crucial at sensitive locations across Thailand. Moreover, the scattered nature of significant trends, shifts, and persistence in a few stations for seasonal and extreme rainfall indicates the existence of substantial unresolved spatial heterogeneity in Thailand.
