http://localhost:8081/jspui/handle/123456789/15066 2026-11-04T02:22:12Z 2026-11-04T02:22:12Z Rawat, Deepak http://localhost:8081/jspui/handle/123456789/21754 2026-10-16T12:35:03Z 2024-09-01T00:00:00Z

Title: SEISMOLOGICAL MONITORING OF LANDSLIDES AND ASSESSMENT IN THE NORTH-WEST HIMALAYAS Authors: Rawat, Deepak Abstract: Predicting seismic signals has always provided significant interest to engineers and seismologists globally. This thesis presents a comprehensive investigation into the seismic monitoring of landslides and related geohazards in the North-West Himalayas, focusing on identifying seismic sources such as landslides, rockslides, and earthquakes through advanced signal analysis techniques. The research highlights the region's vulnerability, characterised by steep slopes and tectonic activity, and addresses the need for improved monitoring systems. The study introduces a multi-stage methodology, starting with the collection and analysis of seismic signals using Time-Frequency Analysis (TFA) techniques, including the Wavelet Synchrosqueezed Transform (WSST), Short-Time Fourier Transform (STFT), and Ensemble Empirical Mode Decomposition (EEMD). The WSST technique provides superior localization and accuracy in identifying dominant frequencies, which is critical for differentiating seismic events. Following noise filtering, statistical and spectral features are extracted and used to classify seismic sources through machine learning models, specifically Random Forest (RF) classifiers. The classifier is trained and validated using a dataset of 175 landslide signals, in combination with earthquake data from the SNATR, demonstrating high accuracy in distinguishing between landslide and earthquake signals. The research also investigates the potential for extending early warning systems based on the findings, suggesting that seismic networks can play a crucial role in real-time hazard detection and disaster risk reduction. Although the dataset for landslide events is limited, the study provides a valuable framework for implementing real-time monitoring and early warning systems in seismically active and topographically challenging regions such as the Himalayas. The thesis highlights the importance of sophisticated seismic monitoring and machine learning methodologies in improving early warning systems for landslides. The study highlights the necessity for enhancing the established approaches, especially by incorporating real-time monitoring data from various seismic networks and diverse geographical areas. Furthermore, the study underscores the need to augment the dataset to encompass a broader range of seismic occurrences, enhancing forecast accuracy. The deployment of these advanced models in operational early warning systems could markedly improve catastrophe preparedness in seismically active areas, with possible applications extending beyond the Himalayas. The study advocates for the investigation of novel features and methods, including AI-driven predictive modelling, to enhance the system's reliability. These developments offer significant potential for creating a resilient, real-time early warning system for landslides and other geohazards, delivering prompt, actionable insights for disaster risk mitigation.

2024-09-01T00:00:00Z Begum, Sumaiyah http://localhost:8081/jspui/handle/123456789/21363 2026-08-27T13:41:12Z 2022-04-01T00:00:00Z

Title: MULTI HAZARD RISK ASSESSMENT FOR GUWAHATI MUNICIPAL CORPORATION AREA Authors: Begum, Sumaiyah

2022-04-01T00:00:00Z Raypa, Shruti http://localhost:8081/jspui/handle/123456789/21279 2026-07-19T17:01:30Z 2023-05-01T00:00:00Z

Title: CRITICAL INFRASTRUCTURE RESILIENCE: A CASE STUDY ON CHAR DHAM ROAD NETWORK Authors: Raypa, Shruti Abstract: Infrastructure is the backbone of any developing nation. A disaster in any region not only disrupts the functioning of the communities in that region but also pushes them many steps backwards. As the nations progress, there is a particular emphasis on infrastructure development and ensuring their resilience so that they develop capacities to bounce back better from any scenario in the most cost efficient way possible. In the 2nd Asian Ministerial Conference on Disaster Risk Reduction (AMCDRR), the importance of Disaster Risk Reduction (DRR) was signified through the Prime Minister’s 10 Point Agenda. In this agenda, a key point is that ‘all development sectors must imbibe the principles of Disaster Risk Management’. Therefore, the principles of disaster risk management must be incorporated in such a way that it contributes not only to the resilience of communities but also of the infrastructures that are built to cater to the needs of the communities. The Sendai Framework for Disaster Risk Reduction (SFDRR) also state that an important agenda is to reduce the disaster damages to critical infrastructures and reduce disruption of basic services. The importance of developing resilience against vulnerabilities is evident through Goal 9 of the UN Sustainable Development Goals (SDG’s). The goal emphasises on building resilient infrastructure, promote inclusive and sustainable industrialization and foster innovations. Moreover, Goal 11 prioritises ‘making cities and human settlements inclusive, safe and resilient’. Sustainable infrastructure in this context, directly and indirectly have linkages with other Sustainable Development Goals. This defines the importance of development of a robust and resilient infrastructure and its role in forming a resilient society. The transportation infrastructure, one of the most critical infrastructures for the state of Uttarakhand, is severely prone to landslide events. This situation arises mainly in the monsoon season when road closures are frequently reported. In recent years, there have been heavy investment towards building resilient road infrastructures under the bigger heading of Char Dham road project. The objectives of the current thesis it is motivated by the fact that the roads are affected by closures during the monsoon season creating heavy traffic as well as disrupting the annual yatra season which causes a ripple effect in the local economy. This thesis aims to achieve the following objectives: First, to examine the best-practices implemented in the all weather road project, also known as the Char Dham Project, and to understand how these practices may promote disaster resilience. Second, to emphasize the significance of resilience through stakeholder surveys and especially with those who have direct or indirect involvement in the project. Finally, to identify the challenge impeding the promotion of resilience and provide potential solutions to address the same. The study identifies factors that may impact the overall infrastructure resilience and elaborates existing gaps and suggestions that could promote resilience of future critical infrastructures being planned and implemented in similar geographies and hazard prone areas.

2023-05-01T00:00:00Z Kumar, Ratan http://localhost:8081/jspui/handle/123456789/21278 2026-07-19T16:59:48Z 2023-05-01T00:00:00Z

Title: Characterizing spatiotemporal dynamics in Surface Urban Heat Island Intensity (SUHII) for cities of India Authors: Kumar, Ratan Abstract: Urban heat island (UHI) affects human health and even cause death. UHI has been studied for the last 40 years, but the study based on climate zone, tiers of the city, and socioecological is significantly less. Moreover, for Indian cities also, their analysis is significantly less. This study includes 76 Indian cities, which includes tier 1 and tier 2 of the city. And the result was correlated to the agroclimatic zone and socioecological aspect. There are six agro-climatic zones in India, i.e., arid, coastal, humid-per humid, island, semi-arid, and sub-humid. However, this study selected cities that fall into five climatic zones except for the island. The study period is 1991, 2001, 2011, and 2021 and it includes two seasons, summer, and winter. Surface urban cool island seen in all four times for arid. All the sciences and theory are satisfied by only Tier 1 cities; Tier 2 cities do not satisfy that relation, but the LST(Urban) of Tier 2 cities is increasing at an alarming rate. In the socioecological aspect, this study includes in-migrants, population NDVI, and NDBI. Cities with higher in-migrants show high LST. LST ranges from 19.65 degrees Celsius to -4.02 degrees Celsius in summer and 7.75 degrees Celsius to -2.8 degrees Celsius in the winter, and in the top ten cities in summer and winter, there is no tier 1 city. LST for summer 68/76 and winter 67/76 cities, but in the case of multi-city analysis, NDVI and NDBI do not show the usual theory. The findings from this research will help for future in-depth research and will finally help in climatic adaptive strategy for different cities in different climatic zones of the city.

2023-05-01T00:00:00Z