Earthquake Multi-Class Detection Using Artificial Intelligence DOI

P. Kavitha,

N. G. Bhuvaneswari Amma,

Roslin Dayana K.

et al.

Advances in environmental engineering and green technologies book series, Journal Year: 2023, Volume and Issue: unknown, P. 115 - 129

Published: Dec. 30, 2023

The goal of the chapter on earthquake multi-magnificence detection and use synthetic intelligence is to discover exhibit usage device learning AI techniques for appropriately efficiently detecting different lessons earthquakes. seeks offer a complete understanding strategies spotlight capability in advancing this subject. present detailed analysis existing recommend novel AI-primarily based that could enhance category accuracy timeliness. Conventional seismology commonly focus earthquakes an unmarried seismic event. However, algorithms can investigate significant quantity information, which includes ancient facts, geological capabilities, actual-time signals, become aware patterns classify into multiple instructions.

Language: Английский

ChatGPT in transforming communication in seismic engineering: Case studies, implications, key challenges and future directions DOI Creative Commons
Partha Pratim Ray

Earthquake Science, Journal Year: 2024, Volume and Issue: 37(4), P. 352 - 367

Published: July 13, 2024

Language: Английский

Citations

3

RETRACTED: Evaluating the impact of climate change and geo‐environmental factors on flood hazards in India: An integrated framework DOI
Indrajit Chowdhuri, Subodh Chandra Pal, Paramita Roy

et al.

Geological Journal, Journal Year: 2023, Volume and Issue: 58(9), P. 3515 - 3543

Published: March 16, 2023

Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, infrastructure throughout the world. The intensity frequency this extreme weather event are expected increase due significant changes in present‐day climate land use cover (LULC) pattern. India has very systematic organized structural program policies but lacks proper implementations, adverse effect change goes on society. This paper an analysis floods hazards LULC patterns. Three models, namely “Eco‐biogeography‐based optimization (EBO), Random forest (RF), Support vector machine (SVM)” were used obtain final output prepare “Flood susceptibility map”. result was validated through “Receiver operating characteristics (ROC)” with “Area under curve (AUC)” values. future rainfall scenario been estimated by considering “General circulation models” different “shared socioeconomic pathways (SSPs)”. values AUC 0.915 0.887 0.869 (SVM), respectively. After consideration SSPs, shows that there increasing tendency flood projected period. all employed modelling approaches, EBO model notable potential delineating possible flood‐prone regions for effective planning management. Decision‐makers can benefit from country‐specific information regional planner implement sustainable long‐term measures overcome type hazardous situation.

Language: Английский

Citations

7

Comprehensive spatial analysis landslide susceptibility modelling, spatial cluster analysis and priority zoning for environment analysis DOI
Heni Masruroh, Listyo Yudha Irawan, Choirul Anam

et al.

International Journal of Environmental Science and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 24, 2024

Language: Английский

Citations

2

Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran DOI Creative Commons

Rahim Tavakolifar,

Himan Shahabi, Mohsen Alizadeh

et al.

Land, Journal Year: 2023, Volume and Issue: 12(6), P. 1151 - 1151

Published: May 30, 2023

Landslides along the main roads in mountains cause fatalities, ecosystem damage, and land degradation. This study mapped susceptibility to landslides Saqqez-Marivan road located Kurdistan province, Iran, comparing an ensemble fuzzy logic with analytic network process (fuzzy logic-ANP; FLANP) TOPSIS logic-TOPSIS; FLTOPSIS) terms of their prediction capacity. First, 100 identified through field surveys were randomly allocated a 70% dataset 30% dataset, respectively, for training validating methods. Eleven landslide conditioning factors, including slope, aspect, elevation, lithology, use, distance fault, river, road, soil type, curvature, precipitation considered. The performance methods was evaluated by inspecting areas under receiver operating curve (AUCROC). accuracies 0.983 0.938, FLTOPSIS FLANP Our findings demonstrate that although both models are known be promising, method had better capacity predicting area. Therefore, map developed is suitable inform management planning prone allocation development purposes, especially mountainous areas.

Language: Английский

Citations

4

Impact of Changing Climate on the Cryospheric Region and Glacier Retreat in the Himalayan Region DOI
Pankaj Kumar,

Deepankshi Shah,

Snigdha Singh

et al.

Sustainable development goals series, Journal Year: 2024, Volume and Issue: unknown, P. 27 - 47

Published: Jan. 1, 2024

Language: Английский

Citations

1

Application of ML- based approach for co-seismic landslides susceptibility mapping and identification of important controlling factors in eastern Himalayan region DOI
Saurav Kumar, Aniruddha Sengupta

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(21)

Published: Oct. 21, 2024

Language: Английский

Citations

1

Compound events of wet and dry extremes: Identification, variations, and risky patterns DOI
Haiyan Chen, Ye Tuo, Chong‐Yu Xu

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 905, P. 167088 - 167088

Published: Sept. 15, 2023

Language: Английский

Citations

3

GIS-Based Landslides Risk Assessment Applying Certainty Factor (CF) and Ensemble with Deep Learning Neural Network (DLNN): a Study of Cachar District of Assam, India DOI
Sk Ajim Ali, Farhana Parvin

Springer proceedings in earth and environmental sciences, Journal Year: 2024, Volume and Issue: unknown, P. 208 - 232

Published: Jan. 1, 2024

Language: Английский

Citations

0

Raising the Agenda of ‘Paradigm Shift’ in Applied Geomorphology: Question(s) or Acceptability DOI
Somenath Halder, Jayanta Das

Geography of the physical environment, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 16

Published: Jan. 1, 2024

Language: Английский

Citations

0

Spatial Prediction of Landslide using Hybrid Multi-Criteria Decision-Making Methods: A Case Study, Saqqez-Marivan Mountain Road DOI Open Access
Himan Shahabi,

Rahim Tavakolifar,

Mohsen Alizadeh

et al.

Published: March 15, 2023

Landslides around the main roads in mountains not only cause fatal events but also ecosystem damage, including land degradation. This study aims to map susceptibility of landslides Saqqez-Marivan rod Kurdistan province, Iran, using ensemble Fuzzy logic with Analytic Network Process (Fuzzy Logic-ANP; FLANP), and TOPSIS Logic-TOPSIS; FLTOPSIS). A total 100 were first recognized by field surveys then they randomly divided into a 70% dataset (70 locations) 30% (30 locations), respectively, for training validating methods. Eleven landslide conditioning factors, slope, aspect, elevation, lithology, use, distance fault, river, road, soil type, curvature, precipitation used. The performance methods was checked areas under receiver operating curve (AUCROC). Results concluded that prediction accuracy based on datasets were, 0.882 0.918 FLANP FLTOPSIS Our findings demonstrated although both models known as promising techniques, method had better capacity predicting studied area. Therefore, developed can be used proper management high potential managers planners during implementation allocation development projects, especially mountainous areas.

Language: Английский

Citations

1