Abstract
Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts’ total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.
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Data availability
The Sentinel 1 SAR data was directly accessed and processed in the Google Earth Engine platform using the link https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD. The MODIS vegetation indices datasets are available on the NASA Earthdata Search Portal (https://search.earthdata.nasa.gov/search). Historical and current gridded climatic datasets are available at Climate Research & Services, IMD, Pune (https://www.imdpune.gov.in/lrfindex.php). The crop area, production, and yield data are found on the https://data.desagri.gov.in/website/crops-apy-report-web portal.
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Acknowledgements
The authors would like to thank the Directorate of Economics and Statistics and the India Meteorological Department, Govt. of India, for providing the necessary data free of cost. The authors would also like to thank the European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA) for freely providing the satellite data required for this research.
Funding
This research is a part of the National Network Project entitled “National Mission for Sustaining the Himalayan Ecosystem (NMSHE)-Taskforce on Agriculture,” funded by the Department of Science and Technology, Government of India, under NMSHE implementation (DST/CCP/TF-6/Phase-2/ICAR/2021(G)), with Indian Agricultural Research Institute as the lead center. The authors would like to thank the funding agency for supporting the research.
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Swadhina Koley: research conceptualization; data curation; formal analysis; writing the original draft. Soora Naresh Kumar.: research conceptualization, manuscript editing, overall supervision.
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Koley, S., Kumar, S.N. Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India. Environ Monit Assess 196, 497 (2024). https://doi.org/10.1007/s10661-024-12667-2
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DOI: https://doi.org/10.1007/s10661-024-12667-2