Einzeltreffer — DigiBib

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 R 2 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.

Titel:	Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.
Autor/in / Beteiligte Person:	Koley, S ; Kumar, SN
Link:	Full Text Finder (Volltext)
Zeitschrift:	Environmental monitoring and assessment, Jg. 196 (2024-05-02), Heft 6, S. 497
Veröffentlichung:	1998- : Dordrecht : Springer ; <i>Original Publication</i>: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981-, 2024
Medientyp:	academicJournal
ISSN:	1573-2959 (electronic)
DOI:	10.1007/s10661-024-12667-2
Schlagwort:	India Crops, Agricultural Oryza Floods Zea mays growth & development Machine Learning Environmental Monitoring methods Agriculture
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Environ Monit Assess] 2024 May 02; Vol. 196 (6), pp. 497. <i>Date of Electronic Publication: </i>2024 May 02. MeSH Terms: Oryza* ; Floods* ; Zea mays* / growth & development ; Machine Learning* ; Environmental Monitoring* / methods ; Agriculture* ; India ; Crops, Agricultural References: Adiba, A., & Bioresita, F. (2023). Sentinel-1 SAR polarization combinations for flood inundation spatial distribution mapping (case study: South Kalimantan). In IOP conference series: Earth and environmental science (Vol. 1127, p. 012009). https://doi.org/10.1088/1755-1315/1127/1/012009. ; Ahmad, I., Saeed, U., Fahad, M., Ullah, A., Habib ur Rahman, M., Ahmad, A., & Judge, J. (2018). Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing, 46(10), 1701–1711. https://doi.org/10.1007/s12524-018-0825-8. (PMID: 10.1007/s12524-018-0825-8) ; Ali, Y. A., Awwad, E. M., Al-Razgan, M., & Maarouf, A. (2023). Hyperparameter search for machine learning algorithms for optimizing the computational complexity. Processes, 11, 349. https://doi.org/10.3390/pr11020349. (PMID: 10.3390/pr11020349) ; Amrani, A., Diepeveen, D., Murray, D., Jones, M. G. K., & Sohel, F. (2024). Multi-task learning model for agricultural pest detection from crop-plant imagery: A Bayesian approach. Computers and Electronics in Agriculture, 218, 108719. https://doi.org/10.1016/j.compag.2024.108719. (PMID: 10.1016/j.compag.2024.108719) ; Bereczky, M., Wieland, M., Krullikowski, C., Martinis, S., & Plank, S. (2022). Sentinel-1-based water and flood mapping: Benchmarking convolutional neural networks against an operational rule-based processing chain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 2023–2036. https://doi.org/10.1109/JSTARS.2022.3152127. (PMID: 10.1109/JSTARS.2022.3152127) ; Bhakta Shrestha, B., Sawano, H., Ohara, M., Yamazaki, Y., & Tokunaga, Y. (2019). Methodology for agricultural flood damage assessment. In Recent advances in flood risk management (pp. 1–19). https://doi.org/10.5772/intechopen.81011. ; Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324. (PMID: 10.1023/A:1010933404324) ; Business Today. (2023). IMD issues red, orange alerts, flash flood warning for Himachal Pradesh for next 24 hours. Business Today. https://www.businesstoday.in/latest/trends/story/imd-issues-red-orange-alerts-flash-flood-warning-for-himachal-pradesh-for-next-24-hours-check-details-here-389099-2023-07-11 . Accessed 20 Oct 2023. ; Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). San Francisco, CA, USA: Association for Computing Machinery, New York, NY, United States. https://doi.org/10.1145/2939672.2939785. ; Chinilin, A., & Savin, I. Y. (2023). Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia. Egyptian Journal of Remote Sensing and Space Science, 26(3), 666–675. https://doi.org/10.1016/j.ejrs.2023.07.007. (PMID: 10.1016/j.ejrs.2023.07.007) ; Clarke, A., Yates, D., Blanchard, C., Islam, M. Z., Ford, R., Rehman, S., & Walsh, R. (2024). The effect of dataset construction and data pre-processing on the extreme gradient boosting algorithm applied to head rice yield prediction in Australia. Computers and Electronics in Agriculture, 219, 108716. https://doi.org/10.1016/j.compag.2024.108716. (PMID: 10.1016/j.compag.2024.108716) ; Clement, M. A., Kilsby, C. G., & Moore, P. (2018). Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 11, 152–168. https://doi.org/10.1111/jfr3.12303. (PMID: 10.1111/jfr3.12303) ; Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964. (PMID: 10.1109/TIT.1967.1053964) ; Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1(3), 131–156. https://doi.org/10.3233/IDA-1997-1302. (PMID: 10.3233/IDA-1997-1302) ; Dimri, A. P., Chevuturi, A., Niyogi, D., Thayyen, R. J., Ray, K., Tripathi, S. N., et al. (2017). Cloudbursts in Indian Himalayas: A review. Earth-Science Reviews, 168, 1–23. https://doi.org/10.1016/j.earscirev.2017.03.006. (PMID: 10.1016/j.earscirev.2017.03.006) ; Drucker, H., Burges, C. J., Kaufman, L., Smola, A. J., & Vapnik, V. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, 9, 155–161. ; Dumont, B., Basso, B., Leemans, V., Bodson, B., Destain, J. P., & Destain, M. F. (2015). A comparison of within-season yield prediction algorithms based on crop model behaviour analysis. Agricultural and Forest Meteorology, 204, 10–21. https://doi.org/10.1016/j.agrformet.2015.01.014. (PMID: 10.1016/j.agrformet.2015.01.014) ; Eriksson, M., Jianchu, X., Shrestha, A. B., & Vaidya, Ramesh Ananda, Nepal, Santosh, Sandstrom, K. (2009). Impact of Climate Change on Water Resources and Livelihoods in the Greater Himalayas. The Changing Himalayas (Vol. 312). Turkey. https://www.preventionweb.net/files/11621_icimodthechanginghimalayas1.pdf . Accessed 12 Oct 2023. ; Georganos, S., Grippa, T., Vanhuysse, S., Lennert, M., Shimoni, M., Kalogirou, S., & Wolff, E. (2018). Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application. Giscience and Remote Sensing, 55(2), 221–242. https://doi.org/10.1080/15481603.2017.1408892. (PMID: 10.1080/15481603.2017.1408892) ; González Perea, R., Fernández García, I., Camacho Poyato, E., & Rodríguez Díaz, J. A. (2023). New memory-based hybrid model for middle-term water demand forecasting in irrigated areas. Agricultural Water Management, 284 https://doi.org/10.1016/j.agwat.2023.108367. ; Govt. of Himachal Pradesh. (2024). Economic Survey 2023–24. https://himachalservices.nic.in/economics/pdf/en-economic_survey_2023-24.pdf . Accessed 12 Apr 2024. ; Guan, H., Huang, J., Li, L., Li, X., Miao, S., Su, W., et al. (2023). Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data. Remote Sensing of Environment, 295, 113714. https://doi.org/10.1016/j.rse.2023.113714. (PMID: 10.1016/j.rse.2023.113714) ; Gupta, V., Syed, B., Pathania, A., Raaj, S., Nanda, A., Awasthi, S., & Shukla, D. P. (2024). Hydrometeorological analysis of July-2023 floods in Himachal Pradesh. Natural Hazards. https://doi.org/10.1007/s11069-024-06520-5. (PMID: 10.1007/s11069-024-06520-5) ; Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. https://doi.org/10.1016/j.aca.2011.07.027. (PMID: 10.1016/j.aca.2011.07.027) ; Habibi, L. N., Matsui, T., & Tanaka, T. S. T. (2024). Critical evaluation of the effects of a cross-validation strategy and machine learning optimization on the prediction accuracy and transferability of a soybean yield prediction model using UAV-based remote sensing. Journal of Agriculture and Food Research, 16, 101096. https://doi.org/10.1016/j.jafr.2024.101096. (PMID: 10.1016/j.jafr.2024.101096) ; Hindustan Times. (2023). 2 dead as heavy rains lash Himachal; landslides, waterlogging across state \| 5 things to know. Hindustan Times. https://www.hindustantimes.com/india-news/himachal-rains-flash-floods-rains-kangra-mandi-kullu-manali-landslide-waterlogging-101687709029847.html . Accessed 16 Oct 2023. ; India Today. (2023a). Red alerts issued in Uttarakhand, Himachal Pradesh amid heavy rain, deaths. India Today. https://www.indiatoday.in/india/video/red-alerts-issued-in-uttarakhand-himachal-pradesh-amid-heavy-rain-deaths-2420907-2023-08-14 . Accessed 16 Oct 2023. ; India Today. (2023b). 81 dead in rain fury in Himachal, Uttarakhand, flash floods in Punjab, rescue efforts on. India Today. New Delhi. https://www.indiatoday.in/india/story/himachal-pradesh-uttarakhand-monsoon-rain-fury-houses-collapse-fresh-landslides-imd-weather-forecast-2422221-2023-08-17 . Accessed 7 Apr 2024. ; Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., et al. (2016). Random forests for global and regional crop yield predictions. PLoS ONE, 11(6), 0156571. https://doi.org/10.1371/journal.pone.0156571. (PMID: 10.1371/journal.pone.0156571) ; Karabulut, E. M., Özel, S. A., & İbrikçi, T. (2012). A comparative study on the effect of feature selection on classification accuracy. Procedia Technology, 1, 323–327. https://doi.org/10.1016/j.protcy.2012.02.068. (PMID: 10.1016/j.protcy.2012.02.068) ; Kathole, A. B., Katti, J., Lonare, S., & Dharmale, G. (2023). Identify and classify pests in the agricultural sector using metaheuristics deep learning approach. Franklin Open, 3, 100024. https://doi.org/10.1016/j.fraope.2023.100024. (PMID: 10.1016/j.fraope.2023.100024) ; Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947–987. https://doi.org/10.1007/s11069-016-2357-2. (PMID: 10.1007/s11069-016-2357-2) ; Kumar, A., Gupta, A. K., Bhambri, R., Verma, A., Tiwari, S. K., & Asthana, A. K. L. (2018). Assessment and review of hydrometeorological aspects for cloudburst and flash flood events in the third pole region (Indian Himalaya). Polar Science, 18, 5–20. https://doi.org/10.1016/j.polar.2018.08.004. (PMID: 10.1016/j.polar.2018.08.004) ; Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., et al. (2023). Crop yield prediction using machine learning models: Case of Irish potato and maize. Agriculture (switzerland), 13, 225. https://doi.org/10.3390/agriculture13010225. (PMID: 10.3390/agriculture13010225) ; Lazin, R., Shen, X., & Anagnostou, E. (2021). Estimation of flood-damaged cropland area using a convolutional neural network. Environmental Research Letters, 16(5), 054011. https://doi.org/10.1088/1748-9326/abeba0. (PMID: 10.1088/1748-9326/abeba0) ; Lee, B. H., Kenkel, P., & Brorsen, B. W. (2013). Pre-harvest forecasting of county wheat yield and wheat quality using weather information. Agricultural and Forest Meteorology, 168, 26–35. https://doi.org/10.1016/j.agrformet.2012.08.010. (PMID: 10.1016/j.agrformet.2012.08.010) ; Lindell, M. K., Arlikatti, S., & Huang, S. K. (2019). Immediate behavioral response to the June 17, 2013 flash floods in Uttarakhand, North India. International Journal of Disaster Risk Reduction, 34, 129–146. https://doi.org/10.1016/j.ijdrr.2018.11.011. (PMID: 10.1016/j.ijdrr.2018.11.011) ; Malla, S. B., Dahal, R. K., & Hasegawa, S. (2020). Analyzing the disaster response competency of the local government official and the elected representative in Nepal. Geoenvironmental Disasters, 7(1), 15. https://doi.org/10.1186/s40677-020-00153-z. (PMID: 10.1186/s40677-020-00153-z) ; Marndi, A., Ramesh, K. V., & Patra, G. K. (2021). Crop production estimation using deep learning technique. Current Science, 121(8), 1073–1079. https://doi.org/10.18520/cs/v121/i8/1073-1079. (PMID: 10.18520/cs/v121/i8/1073-1079) ; Mishra, P. K., Thayyen, R. J., Singh, H., Das, S., Nema, M. K., & Kumar, P. (2022). Assessment of cloudbursts, extreme rainfall and vulnerable regions in the Upper Ganga basin, Uttarakhand, India. International Journal of Disaster Risk Reduction, 69, 102744. https://doi.org/10.1016/j.ijdrr.2021.102744. (PMID: 10.1016/j.ijdrr.2021.102744) ; Mohammed, S., Arshad, S., Bashir, B., Vad, A., Alsalman, A., & Harsányi, E. (2024). Machine learning driven forecasts of agricultural water quality from rainfall ionic characteristics in Central Europe. Agricultural Water Management, 293. https://doi.org/10.1016/j.agwat.2024.108690. ; Monteleone, B., Giusti, R., Magnini, A., Arosio, M., Domeneghetti, A., Borzì, I., et al. (2023). Estimations of crop losses due to flood using multiple sources of information and models: The case study of the Panaro River. Water (switzerland), 15, 1980. https://doi.org/10.3390/w15111980. (PMID: 10.3390/w15111980) ; Nhangumbe, M., Nascetti, A., Georganos, S., & Ban, Y. (2023). Supervised and unsupervised machine learning approaches using Sentinel data for flood mapping and damage assessment in Mozambique. Remote Sensing Applications: Society and Environment, 32, 101015. https://doi.org/10.1016/j.rsase.2023.101015. (PMID: 10.1016/j.rsase.2023.101015) ; Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076. (PMID: 10.1109/TSMC.1979.4310076) ; Panigrahi, B., Kathala, K. C. R., & Sujatha, M. (2023). A machine learning-based comparative approach to predict the crop yield using supervised learning with regression models. Procedia Computer Science, 218, 2684–2693. https://doi.org/10.1016/j.procs.2023.01.241. (PMID: 10.1016/j.procs.2023.01.241) ; Pasley, H. R., Huber, I., Castellano, M. J., & Archontoulis, S. V. (2020). Modeling flood-induced stress in soybeans. Frontiers in Plant Science, 11, 62. https://doi.org/10.3389/fpls.2020.00062. (PMID: 10.3389/fpls.2020.00062) ; Prashar, R. (2023). Himachal Pradesh farmers struggle to rebuild after flood destroys crops, leaves behind debris. 101 Reporter. Mandi. https://101reporters.com/article/agriculture/Himachal_Pradesh_farmers_struggle_to_rebuild_after_flood_destroys_crops_leaves_behind_debris . Accessed 11 Apr 2024. ; Qamer, F. M., Abbas, S., Ahmad, B., Hussain, A., Salman, A., Muhammad, S., et al. (2023). A framework for multi-sensor satellite data to evaluate crop production losses: The case study of 2022 Pakistan floods. Scientific Reports, 13(1), 4240. https://doi.org/10.1038/s41598-023-30347-y. (PMID: 10.1038/s41598-023-30347-y) ; Rahman, M. S., Di, L., Yu, E., Lin, L., & Yu, Z. (2021). Remote sensing based rapid assessment of flood crop damage using Novel Disaster Vegetation Damage Index (DVDI). International Journal of Disaster Risk Science, 12(1), 90–110. https://doi.org/10.1007/s13753-020-00305-7. (PMID: 10.1007/s13753-020-00305-7) ; Rawat, K. S., Sahu, S. R., Singh, S. K., & Mishra, A. K. (2022). Cloudburst analysis in the Nainital district, Himalayan Region, 2021. Discover Water, 2(1), 12. https://doi.org/10.1007/s43832-022-00020-y. (PMID: 10.1007/s43832-022-00020-y) ; Sadek, M., Li, X., Mostafa, E., Freeshah, M., Kamal, A., Sidi Almouctar, M. A., et al. (2020). Low-cost solutions for assessment of flash flood impacts using Sentinel-1/2 data fusion and hydrologic/hydraulic modeling: Wadi El-Natrun region. Egypt. Advances in Civil Engineering, 2020, 1039309. https://doi.org/10.1155/2020/1039309. (PMID: 10.1155/2020/1039309) ; Shrestha, B. B., Kawasaki, A., & Zin, W. W. (2021). Development of flood damage functions for agricultural crops and their applicability in regions of Asia. Journal of Hydrology: Regional Studies, 36, 100872. https://doi.org/10.1016/j.ejrh.2021.100872. (PMID: 10.1016/j.ejrh.2021.100872) ; Shrestha, R., Shao, Y., Di, L., Kang, L., Yu, G., & Zhang, B. (2013). Detection of flood and its impact on crops using NDVI - Corn Case. In 2013 2nd International Conference on Agro-Geoinformatics: Information for Sustainable Agriculture, Agro-Geoinformatics 2013 (pp. 200–204). https://doi.org/10.1109/Argo-Geoinformatics.2013.6621907. ; Sim, K. B., Lee, M. L., & Wong, S. Y. (2022). A review of landslide acceptable risk and tolerable risk. Geoenvironmental Disasters, 9(1), 3. https://doi.org/10.1186/s40677-022-00205-6. (PMID: 10.1186/s40677-022-00205-6) ; Tarpanelli, A., Mondini, A. C., & Camici, S. (2022). Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe. Natural Hazards and Earth System Sciences, 22, 2473–2489. https://doi.org/10.5194/nhess-22-2473-2022. (PMID: 10.5194/nhess-22-2473-2022) ; The Economic Times. (2023, July 11). Monsoon mayhem: Rain continues to batter north India; Himachal worst-hit. The Economic Times. https://economictimes.indiatimes.com/news/india/monsoon-mayhem-rain-continues-to-batter-north-india-himachal-worst-hit/videoshow/101660998.cms?from=mdr . Accessed 16 Oct 2023. ; The Times of India. (2023a). Heavyrainfall: Himachal, Punjab, Haryana, Uttarakhand grapple with flood fury, landslides; national highways washed away. The Times of India. https://timesofindia.indiatimes.com/city/dehradun/heavyrainfall-himachal-punjab-haryana-uttarakhand-grapple-with-flood-fury-landslides-national-highways-washed-away/articleshow/101653540.cms?from=mdr. ; The Times of India. (2023b). Destruction pours in Himachal Pradesh: Death toll reaches 242; 400 roads blocked; red alert for 6 districts, flash flood warning for 9. The Times of India. Shimla. https://timesofindia.indiatimes.com/city/shimla/destruction-pours-in-himachal/articleshow/103002375.cms?from=mdr . Accessed 11 Apr 2024. ; Uddin, K., Matin, M. A., & Meyer, F. J. (2019). Operational flood mapping using multi-temporal Sentinel-1 SAR images: A case study from Bangladesh. Remote Sensing, 11, 1581. https://doi.org/10.3390/rs11131581. (PMID: 10.3390/rs11131581) ; Wang, X., Liu, Z., & Chen, H. (2022). Investigating flood impact on crop production under a comprehensive and spatially explicit risk evaluation framework. Agriculture (switzerland), 12, 484. https://doi.org/10.3390/agriculture12040484. (PMID: 10.3390/agriculture12040484) ; Win, S., Zin, W. W., Kawasaki, A., & San, Z. M. L. T. (2018). Establishment of flood damage function models: A case study in the Bago River Basin, Myanmar. International Journal of Disaster Risk Reduction, 28, 688–700. https://doi.org/10.1016/j.ijdrr.2018.01.030. (PMID: 10.1016/j.ijdrr.2018.01.030) ; Woolley, R., Marsell, R., & Grover, N. (1946). Cloudburst floods in Utah, 1850–1938. Water-Supply Paper 994. http://pubs.usgs.gov/wsp/0994/report.pdf . Accessed 16 Oct 2023. ; Yulita, I. N., Rambe, M. F. R., Sholahuddin, A., & Prabuwono, A. S. (2023). A convolutional neural network algorithm for pest detection using GoogleNet. AgriEngineering, 5(4), 2366–2380. https://doi.org/10.3390/agriengineering5040145. (PMID: 10.3390/agriengineering5040145) ; Zayani, H., Fouad, Y., Michot, D., Kassouk, Z., Baghdadi, N., Vaudour, E., et al. (2023). Using machine-learning algorithms to predict soil organic carbon content from combined remote sensing imagery and laboratory Vis-NIR spectral datasets. Remote Sensing, 15(17). https://doi.org/10.3390/rs15174264. Grant Information: DST/CCP/TF-6/Phase-2/ICAR/2021(G) Department of Science and Technology, Ministry of Science and Technology, India; DST/CCP/TF-6/Phase-2/ICAR/2021(G) Department of Science and Technology, Ministry of Science and Technology, India Contributed Indexing: Keywords: Crop production loss; Flash flood; Indian Himalayan Region; Machine learning; Regression; Satellite data Entry Date(s): Date Created: 20240502 Date Completed: 20240502 Latest Revision: 20240620 Update Code: 20240620

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

BibTeX Citavi, JabRef, u.a.
(Literaturverwaltung)

PDF kein Volltext!
(Merkzettel, Notizen)

RIS Endnote, Citavi u.a.
(Literaturverwaltung)

MODS
(XML zur Weiterverarbeitung)

oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

Gewünschter Zitations-Stil:

oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.