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Kumar, Rohan
- Landslide Susceptibility Zonation of Tehri Reservoir Rim Region Using Binary Logistic Regression Model
Abstract Views :223 |
PDF Views:124
Authors
Rohan Kumar
1,
R. Anbalagan
1
Affiliations
1 Department of Earth Sciences, Indian Institute of Technology, Roorkee 247 667, IN
1 Department of Earth Sciences, Indian Institute of Technology, Roorkee 247 667, IN
Source
Current Science, Vol 108, No 9 (2015), Pagination: 1662-1672Abstract
A remote sensing and GIS based landslide susceptibility zonation (LSZ) of the Tehri reservoir rim region has been presented here. Landslide causal factors such as land use/land cover, photo-lineaments, landslide incidences, drainage, slope, aspect, relative relief, topographic wetness index and stream power index were derived from remote sensing data. Ancillary data included published geological map, soil map and topographic map. Correlation between factor classes and landslides was computed using binary logistic regression model and a probability estimate of landslide occurrence on cell-by-cell basis for the entire study area was obtained. The probability map was further classified into very low, low, moderate, high and very high susceptible zones using statistical class break technique. Accuracy assessment of the model was performed using ROC curve technique, which in turn gave acceptable 80.2% accuracy. LSZ indicates that the area immediate to the reservoir side slope is highly prone to landslides.Keywords
Logistic Regression, Landslide Susceptibility Zonation, Remote Sensing, Reservoir Rim.Full Text
- Utilizing Machine Learning Algorithm, Cloud Computing Platform and Remote Sensing Satellite Data for Impact Assessment of Flood on Agriculture Land
Abstract Views :46 |
PDF Views:41
Authors
Affiliations
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 Lovely Professional University, Phagwara 144 001, IN
3 Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, IN
4 Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, IN
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 Lovely Professional University, Phagwara 144 001, IN
3 Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, IN
4 Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, IN
Source
Current Science, Vol 125, No 8 (2023), Pagination: 886-895Abstract
Floods are one of the most devastating natural disasters that cause immense damage to life, property and agriculture worldwide. Recurring floods in Bihar (a state in eastern India) during the monsoon season impact the agro-based economy, destroying crops and making it difficult for farmers to prepare for the next season. To mitigate the impact of floods on the agricultural sector, there is a need for early warning systems. Nowadays, remote sensing technology is used extensively for monitoring and managing flood events, which is also used in the present study. The random forest (RF) machine learning (ML) algorithm has also been used for land-use classification, and its output is used as an input for flood impact assessment. Here, we have analysed the flood extents and their impact on agriculture using Sentinel-1 SAR, Sentinel-2 and Planet Scope optical imageries on the Google Earth Engine (GEE) cloud computing platform. The present study shows that floods severely impacted a large part of Bihar during the monsoon seasons of 2020 and 2021. About 701,967 ha of land (614,706 ha agricultural land) in 2020 and 955,897 ha (851,663 ha agricultural land) in 2021 were severely flooded. An inundation maps and area statistics have been generated to visualise the results, which can help the government authorities prioritize relief and rescue operations.Keywords
Agriculture, Cloud Computing Platforms, Floods, Machine Learning Algorithm, Remote Sensing Data.Full Text
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