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Utilizing Machine Learning Algorithm, Cloud Computing Platform and Remote Sensing Satellite Data for Impact Assessment of Flood on Agriculture Land


Affiliations
1 ICAR-National Dairy Research Institute, Karnal 132 001, India
2 Lovely Professional University, Phagwara 144 001, India
3 Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, India
4 Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, India
 

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.
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  • Utilizing Machine Learning Algorithm, Cloud Computing Platform and Remote Sensing Satellite Data for Impact Assessment of Flood on Agriculture Land

Abstract Views: 182  |  PDF Views: 85

Authors

Himanshu Kumar
ICAR-National Dairy Research Institute, Karnal 132 001, India
Rohan Kumar
Lovely Professional University, Phagwara 144 001, India
Sujay Dutta
Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, India
Magan Singh
ICAR-National Dairy Research Institute, Karnal 132 001, India
Sateesh Kr. Karwariya
Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, India

Abstract


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.

References





DOI: https://doi.org/10.18520/cs%2Fv125%2Fi8%2F886-895