Scopus Index (2025)
Disaster Advances (ISSN:0974-262X)
Disaster Advances (ISSN:0974-262X) is a monthly peer-reviewed scopus-indexed journal from 2009 to present. The publisher of this journal is World Researchers Associations. Disaster Advances (ISSN:0974-262X) committed to gathering and disseminating excellent research achievements. The journal welcomes all types of Engineering journal includes Social Sciences: Geography, Planning and Development, Engineering: Safety, Risk, Reliability and Quality, Environmental Science: Environmental Science (miscellaneous), Earth and Planetary Sciences: Earth and Planetary Sciences (miscellaneous) .
Submission Dateline
( Vol 18 , Issue 04 ) | 14 Sep 2025
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Publication Date
( Vol 18 , Issue 04 ) | 30 Sep 2025
Aim And Scopes
Disaster Advances (ISSN:0974-262X)
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1. Social Sciences: Geography, Planning and Development
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2. Engineering: Safety, Risk, Reliability and Quality
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3. Environmental Science: Environmental Science (miscellaneous)
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4. Earth and Planetary Sciences: Earth and Planetary Sciences (miscellaneous)
Latest Journal Here
Disaster Advances (ISSN:0974-262X)
Dependence Modeling for Tsunami and Earthquake Disasters in the Indonesian Megathrust Zones using Copula Models
The megathrust earthquake parameters that are related to the number of tsunami disasters (π) are the number of earthquakes (π) and the maximum earthquake magnitude (π). The dependence property of these three random variables is stochastic due to the uncertainty of each event. This study provides the stochastic dependence model of (π, π) and (π, π) based on the historical da
read moreAnalyzing Current Trends and forecasting Snow Cover Dynamics: A Multi-Data Approach utilizing Satellite Data, ERA5 and SSP Scenarios for Enhanced Disaster Preparedness
The Indus Himalayan region, characterized by its challenging mountainous terrain, presents obstacles for ground observations. This study aims to investigate variations in Snow Cover Area (SCA) and its relationship with other climatic variables. We utilized MODIS snow cover, temperature, ECMWF ERA-5 and SRTM elevation products to assess present trends (2002-2023) and CMIP6 SSP scenarios (SSP1-2.
read moreComparative Analysis of Machine Learning Models and Hybrid Ensemble Approachβs for Landslide Prediction
The NH-44 Jammu Srinagar National Highway in India is susceptible to landslides, rock falls and shooting stones due to its geological characteristics and steep slopes. This study aims to compare the performance of various Machine Learning (ML) algorithms and hybrid models in predicting landslides using historical data. Seven optimized ML approaches Support Vector Classifier (SVC), Logistic Regr
read moreRainwater Flood risk assessment in Zaatari Refugees Camp-Jordan: Towards Sustainable Solutions
Refugee settlements are frequently located in isolated and remote areas, characterized by poor-quality land and harsh climatic conditions. Zaatari camp in Jordan has suffered and continues to suffer from rainwater floods every winter. This study uses GIS analysis to assess flood risk in the camp, revealing that 61.1% of the area is moderately risky, while 0.84% and 22.42% are very high and high
read moreAssessing Future hydrological response of an urban watershed using machine learning based LULC forecasting models
Urbanization in terms of land-use/cover (LULC) change has a long-term significant impact on the hydrological cycle as the LULC is one of the most important influencing parameters to produce curve number (CN). The drastic change in LULC changes the CN. This change directly affects surface water including peak flows. This study aims to assess the change in surface runoff due to changes in LULC. H
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