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Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms

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Abstract

Droughts are particularly disastrous in South Africa and other arid regions that are water-scarce by nature due to low rainfall and water sources. According to some studies, droughts are not uncommon in Africa's drylands and have been rising in dry African terrain. Warm to hot summers and cool to cold winters describe the climate of the Free State Province, South Africa, a province that has been severely affected by drought events in recent times. Several studies have been carried out as regards drought prediction and mapping in arid and semi-arid areas using various models, tools and techniques. However, the use of machine learning algorithms is just emerging, especially in Sub-Saharan Africa. Studies have shown that machine learning and artificial intelligence methods have a high potential for assessment, prediction and identification of extreme events such as drought. Hence, this study aimed to evaluate drought dynamics in the Free State Province and identify drought drivers using regression-based algorithms. Results revealed that 2015 was severely affected by drought episodes as the study area observed extreme drought. More so, findings from this study showed that agricultural lands, cultivated grasslands, and barren surfaces were influenced or impacted by the drought disaster, especially in 2015, a drought year in the Free State Province. From the feature selection results, the influence of climate proxies and anthropogenic factors on VCI shows the ecological situation within the Free State Province.

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References

  • Abbas S, Nichol JE, Qamer FM, Xu J (2014) Characterization of drought development through remote sensing: a case study in Central Yunnan. China Remote Sens 6(6):4998–5018

    Article  Google Scholar 

  • Abubakar HB, Newete SW, Scholes MC (2020) Drought characterization and trend detection using the reconnaissance drought index for setsoto municipality of the free state province of South Africa and the impact on maize yield. Water 12(11):2993

    Article  Google Scholar 

  • Adedeji O, Olusola A, James G, Shaba HA, Orimoloye IR, Singh SK, Adelabu S (2020) Early warning systems development for agricultural drought assessment in Nigeria. Environ Monit Assess 192(12):1–21

    Article  Google Scholar 

  • Akinyemi FO (2017) Climate change and variability in semiarid palapye, eastern botswana: an assessment from smallholder farmers’ perspective. Weather, Climate, Soc 9(3):349–365

    Article  Google Scholar 

  • Akinyemi FO (2021) Vegetation trends, drought severity and land use-land cover change during the growing season in semi-arid contexts. Remote Sens 13(5):836

    Article  Google Scholar 

  • Albano CM, McGwire KC, Hausner MB, McEvoy DJ, Morton CG, Huntington JL (2020) Drought sensitivity and trends of riparian vegetation vigor in Nevada, USA (1985–2018). Remote Sensing 12(9):1362

    Article  Google Scholar 

  • AppEEARS Team (2020) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). Ver. 2.44. NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. https://lpdaacsvc.cr.usgs.gov/appeears

  • Arastoo, B. (2013). The drought effects on land cover and land use evaluating by GIS and RS image processing in Northern Part Of Iran.

  • Awad M, & Khanna R. (2015). Support vector regression. In Efficient learning machines (pp. 67–80). Apress, Berkeley, CA.

  • Azadi H, Keramati P, Taheri F, Rafiaani P, Teklemariam D, Gebrehiwot K, Witlox F (2018) Agricultural land conversion: reviewing drought impacts and coping strategies. Int J Disaster Risk Reduct 31:184–195

    Article  Google Scholar 

  • Baudoin MA, Vogel C, Nortje K, Naik M (2017) Living with drought in South Africa: lessons learnt from the recent El Niño drought period. Int J Disaster Risk Reduct 23:128–137

    Article  Google Scholar 

  • Bechtel B, Demuzere M, & Stewart I. D. (2020). A weighted accuracy measure for land cover mapping: comment on Johnson et al. local climate zone (LCZ) map accuracy assessments should account for land cover physical characteristics that affect the local thermal environment. Remote Sens. 2019, 11, 2420. Remote Sensing, 12(11), 1769.

  • Beg, A. A. F., & Al-Sulttani, A. H. (2020). Spatial assessment of drought conditions over Iraq using the standardized precipitation index (SPI) and GIS techniques. In Environmental remote sensing and GIS in Iraq (pp. 447–462). Springer, Cham.

  • Belayneh, A., & Adamowski, J. (2013). Drought forecasting using new machine learning methods. J Water Land Dev.

  • Boguszewska-Mańkowska D, Pieczyński M, Wyrzykowska A, Kalaji HM, Sieczko L, Szweykowska-Kulińska Z, Zagdańska B (2018) Divergent strategies displayed by potato (Solanum tuberosum L.) cultivars to cope with soil drought. J Agron Crop Sci 204(1):13–30

    Article  Google Scholar 

  • Botai CM, Botai JO, Dlamini LC, Zwane NS, Phaduli E (2016) Characteristics of droughts in South Africa: a case study of free state and North West Provinces. Water 8(10):439

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Brown I, Poggio L, Gimona A, Castellazzi M (2011) Climate change, drought risk and land capability for agriculture: implications for land use in Scotland. Reg Environ Change 11(3):503–518

    Article  Google Scholar 

  • Burke EJ (2011) Understanding the sensitivity of different drought metrics to the drivers of drought under increased atmospheric CO2. J Hydrometeorol 12(6):1378–1394

    Article  Google Scholar 

  • Byakatonda J, Parida BP, Kenabatho PK, Moalafhi DB (2019) Prediction of onset and cessation of austral summer rainfall and dry spell frequency analysis in semiarid Botswana. Theoret Appl Climatol 135(1):101–117

    Article  Google Scholar 

  • Carter MR, Little PD, Mogues T, Negatu W (2006) Shocks, sensitivity and resilience: Tracking the economic impacts of environmental disaster on assets in Ethiopia and Honduras (No:580–2016-39326)

    Google Scholar 

  • Cartwright JM, Littlefield CE, Michalak JL, Lawler JJ, Dobrowski SZ (2020) Topographic, soil, and climate drivers of drought sensitivity in forests and shrublands of the Pacific Northwest, USA. Sci Rep 10(1):1–13

    Article  Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250

    Article  Google Scholar 

  • Conradie SR, Woodborne SM, Cunningham SJ, McKechnie AE (2019) Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc Natl Acad Sci 116(28):14065–14070

    Article  Google Scholar 

  • Cooperative Governance and Traditional Affairs (2016) Drought situation in Free State: water affairs briefing; State of South African Cities 2016: South African Cities Network briefing (https://pmg.org.za/committee-meeting/23605/)

  • Dai M, Huang S, Huang Q, Leng G, Guo Y, Wang L, Zheng X (2020) Assessing agricultural drought risk and its dynamic evolution characteristics. Agric Water Manag 231:106003

    Article  Google Scholar 

  • Downing, T. E., & Bakker, K. (2000). Drought risk in a changing environment. In Drought and drought mitigation in europe (pp. 79–90). Springer, Dordrecht.

  • Engelbrecht F, Adegoke J, Bopape MJ, Naidoo M, Garland R, Thatcher M, Gatebe C (2015) Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ Res Lett 10(8):085004

    Article  Google Scholar 

  • Felsche E, Ludwig R (2021) Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations. Nat Hazard 21(12):3679–3691

    Article  Google Scholar 

  • Feng P, Wang B, Li Liu D, Yu Q (2019) Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia. Agric Syst 173:303–316

    Article  Google Scholar 

  • Friedman J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 1–67.

  • Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17

    Article  Google Scholar 

  • Ghasemi P, Karbasi M, Nouri AZ, Tabrizi MS, Azamathulla HM (2021) Application of Gaussian process regression to forecast multi-step ahead SPEI drought index. Alex Eng J 60(6):5375–5392

    Article  Google Scholar 

  • Greenwell BM (2017) pdp: an R package for constructing partial dependence plots. R J 9(1):421

    Article  Google Scholar 

  • Greenwell BM, Boehmke BC, Gray B (2020) Variable importance plots-an introduction to the vip package. R J 12(1):343

    Article  Google Scholar 

  • Grömping U (2009) Variable importance assessment in regression: linear regression versus random forest. Am Stat 63(4):308–319

    Article  Google Scholar 

  • Haile GG, Tang Q, Sun S, Huang Z, Zhang X, Liu X (2019) Droughts in East Africa: causes, impacts and resilience. Earth Sci Rev 193:146–161

    Article  Google Scholar 

  • Hassan Z, Shabbir R, Ahmad SS, Malik AH, Aziz N, Butt A, Erum S (2016) Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan. Springerplus 5(1):1–11

    Article  Google Scholar 

  • He X, Estes L, Konar M, Tian D, Anghileri D, Baylis K, Sheffield J (2019) Integrated approaches to understanding and reducing drought impact on food security across scales. Curr Opin Environ Sustain 40:43–54

    Article  Google Scholar 

  • Hermans K, McLeman R (2021) Climate change, drought, land degradation and migration: exploring the linkages. Curr Opin Environ Sustain 50:236–244

    Article  Google Scholar 

  • Jamali A (2019) Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran. SN Appl Sci 1(11):1–11

    Article  Google Scholar 

  • Jin J, Wang Q, Li L (2016) Long-term oscillation of drought conditions in the western China: an analysis of PDSI on a decadal scale. J Arid Land 8(6):819–831

    Article  Google Scholar 

  • Jolliffe IT (1982) A note on the use of principal components in regression. J Roy Stat Soc: Ser C (appl Stat) 31(3):300–303

    Google Scholar 

  • Kisi O, Parmar KS (2016) Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. J Hydrol 534:104–112

    Article  Google Scholar 

  • Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull Am Meteorol Soc 76(5):655–668

    Article  Google Scholar 

  • Kogo BK, Kumar L, Koech R (2021) Climate change and variability in Kenya: a review of impacts on agriculture and food security. Environ Dev Sustain 23(1):23–43

    Article  Google Scholar 

  • Krange O, Kaltenborn BP, Hultman M (2019) Cool dudes in Norway: climate change denial among conservative Norwegian men. Environ Sociol 5(1):1–11

    Article  Google Scholar 

  • Kuhn M (2008) Building Predictive Models in R Using the caret Package. J Stat Softw 28(5):1–26. https://doi.org/10.18637/jss.v028.i05

    Article  Google Scholar 

  • Li K, Tong Z, Liu X, Zhang J, Tong S (2020) Quantitative assessment and driving force analysis of vegetation drought risk to climate change: methodology and application in Northeast China. Agric for Meteorol 282:107865

    Article  Google Scholar 

  • Li J, Wang Z, Wu X, Xu CY, Guo S, Chen X, Zhang Z (2021) Robust meteorological drought prediction using antecedent SST fluctuations and machine learning. Water Resources Research 57(8):e2020WR029413

    Article  Google Scholar 

  • Liu X, Zhu X, Zhang Q, Yang T, Pan Y, Sun P (2020) A remote sensing and artificial neural network-based integrated agricultural drought index: index development and applications. CATENA 186:104394

    Article  Google Scholar 

  • Luedeling E, Guo L, Dai J, Leslie C, Blanke MM (2013) Differential responses of trees to temperature variation during the chilling and forcing phases. Agric Meteorol 181:33–42

    Article  Google Scholar 

  • Lunyolo LD, Khalifa M, Ribbe L (2021) Assessing the interaction of land cover/land use dynamics, climate extremes and food systems in Uganda. Sci Total Environ 753:142549

    Article  Google Scholar 

  • Madasa A, Orimoloye IR, Ololade OO (2021) Application of geospatial indices for mapping land cover/use change detection in a mining area. J African Earth Sci 175:104108

    Article  Google Scholar 

  • Mahlstein I, Daniel JS, Solomon S (2013) Pace of shifts in climate regions increases with global temperature. Nat Clim Change 3(8):739–743

    Article  Google Scholar 

  • Mare F, Bahta YT, Van Niekerk W (2018) The impact of drought on commercial livestock farmers in South Africa. Dev Pract 28(7):884–898

    Article  Google Scholar 

  • Marengo JA, Cunha APM, Nobre CA, Neto GGR, Magalhaes AR, Torres RR, Álvala RC (2020) Assessing drought in the drylands of northeast Brazil under regional warming exceeding 4 C. Nat Hazards 103(2):2589–2611

    Article  Google Scholar 

  • McGovern A, Elmore KL, Gagne DJ, Haupt SE, Karstens CD, Lagerquist R, Williams JK (2017) Using artificial intelligence to improve real-time decision-making for high-impact weather. Bull Am Meteor Soc 98(10):2073–2090

    Article  Google Scholar 

  • Mera GA (2018) Drought and its impacts in Ethiopia. Weather Clim Extremes 22:24–35

    Article  Google Scholar 

  • Moeletsi ME, Walker S (2012) Assessment of agricultural drought using a simple water balance model in the free State Province of South Africa. Theoret Appl Climatol 108(3):425–450

    Article  Google Scholar 

  • Musolino DA, Massarutto A, de Carli A (2018) Does drought always cause economic losses in agriculture? An empirical investigation on the distributive effects of drought events in some areas of Southern Europe. Sci Total Environ 633:1560–1570

    Article  Google Scholar 

  • Muthelo D, Owusu-Sekyere E, Ogundeji AA (2019) Smallholder farmers’ adaptation to drought: identifying effective adaptive strategies and measures. Water 11(10):2069

    Article  Google Scholar 

  • Nabil M, Zhang M, Bofana J, Wu B, Stein A, Dong T, Shang J (2020) Assessing factors impacting the spatial discrepancy of remote sensing based cropland products: a case study in Africa. Int J Appl Earth Observation Geoinf 85:102010

    Article  Google Scholar 

  • Orimoloye IR, Ololade OO, Mazinyo SP, Kalumba AM, Ekundayo OY, Busayo ET, Nel W (2019) Spatial assessment of drought severity in Cape Town area, South Africa. Heliyon 5(7):e02148

    Google Scholar 

  • Orimoloye IR, Ololade OO, Belle JA (2021a) Satellite-based application in drought disaster assessment using terra MOD13Q1 data across free state province, South Africa. J Environ Manag 285:112112

    Article  Google Scholar 

  • Orimoloye IR, Belle JA, Ololade OO (2021b) Drought disaster monitoring using MODIS derived index for drought years: a space-based information for ecosystems and environmental conservation. J Environ Manag 284:112028

    Article  Google Scholar 

  • Orimoloye IR, Olusola AO, Ololade O, Adelabu S (2021c) A persistent fact: reflections on drought severity evaluation over Nigerian sahel using MOD13Q1. Arab J Geosci. https://doi.org/10.1007/s12517-021-08369-5

    Article  Google Scholar 

  • Osaka S, Painter J, Walton P, Halperin A (2020) Media representation of extreme event attribution: a case study of the 2011–17 California drought. Weather, Climate, Soc 12(4):847–862

    Article  Google Scholar 

  • Otto FE, Wolski P, Lehner F, Tebaldi C, Van Oldenborgh GJ, Hogesteeger S, New M (2018) Anthropogenic influence on the drivers of the Western Cape drought 2015–2017. Environ Res Lett 13(12):124010

    Article  Google Scholar 

  • Pande CB, Moharir KN, Khadri SFR et al (2018) Study of land use classification in an arid region using multispectral satellite images. Appl Water Sci 8:123

    Article  Google Scholar 

  • Pande CB, Moharir KN, Khadri SFR (2021) Assessment of land-use and land-cover changes in Pangari watershed area (MS), India, based on the remote sensing and GIS techniques. Appl Water Sci 11:96

    Article  Google Scholar 

  • Panu US, Sharma TC (2002) Challenges in drought research: some perspectives and future directions. Hydrol Sci J 47(S1):S19–S30

    Article  Google Scholar 

  • Prasetyo S. Y. J, Hartomo K. D, Paseleng M. C, Candra D. W, & Simanjuntak B. H. (2019, July). The machine learning to detect drought risk in central java using landsat 8 OLI remote sensing images. In 2019 5th International conference on science and technology (ICST) (Vol. 1, pp. 1–6). IEEE.

  • Quan Q, Hao Z, Xifeng H, & Jingchun L. (2020). Research on water temperature prediction based on improved support vector regression. Neural Comput Appl, 1–10.

  • R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/.

  • Raev I, & Rosnev B (2018). The impact of drought on natural forest ecosystems. In Drought in bulgaria (pp. 117–136). Routledge.

  • Rhee J, Im J (2017) Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agric for Meteorol 237:105–122

    Article  Google Scholar 

  • Ribeiro AF, Russo A, Gouveia CM, Páscoa P (2019) Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices. Theoret Appl Climatol 136(1–2):203–220

    Article  Google Scholar 

  • Riebsame WE, Changnon SA, Karl TR (2019) Drought and natural resources management in the United States: Impacts and Implications of the 1987–89 Drought. Routledge

    Book  Google Scholar 

  • Rustad SA, Rosvold EL, Buhaug H (2020) Development aid, drought, and coping capacity. J Develop Stud 56(8):1578–1593

    Article  Google Scholar 

  • Shiau JT, Lin JW (2016) Clustering quantile regression-based drought trends in Taiwan. Water Resour Manage 30(3):1053–1069

    Article  Google Scholar 

  • Slette IJ, Post AK, Awad M, Even T, Punzalan A, Williams S, Knapp AK (2019) How ecologists define drought, and why we should do better. Glob Change Biol 25(10):3193–3200

    Article  Google Scholar 

  • Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  Google Scholar 

  • Sousa PM, Blamey RC, Reason CJ, Ramos AM, Trigo RM (2018) The ‘day zero’cape town drought and the poleward migration of moisture corridors. Environ Res Lett 13(12):124025

    Article  Google Scholar 

  • Sprain L (2017) Paradoxes of public participation in climate change governance. Good Soc 25(1):62–80

    Article  Google Scholar 

  • Steyn M, Meissner R, Nortje K, Funke N, & Petersen C (2019). Water security and South Africa. In Understanding water security at local government level in South Africa (pp. 1–44). Palgrave Pivot, Cham.

  • Sun F, Mejia A, Zeng P, Che Y (2019) Projecting meteorological, hydrological and agricultural droughts for the Yangtze River basin. Sci Total Environ 696:134076

    Article  Google Scholar 

  • Sweet SK, Wolfe DW, DeGaetano A, Benner R (2017) Anatomy of the 2016 drought in the Northeastern United States: implications for agriculture and water resources in humid climates. Agric for Meteorol 247:571–581

    Article  Google Scholar 

  • Swemmer AM, Bond WJ, Donaldson J, Hempson GP, Malherbe J, Smit IP (2018) The ecology of drought-a workshop report. S Afr J Sci 114(9–10):1–3

    Google Scholar 

  • Tobias, R. D. (1995, April). An introduction to partial least squares regression. In Proceedings of the twentieth annual SAS users group international conference (Vol. 20). Cary: SAS Institute Inc.

  • Trnka M, Semerádová D, Novotný I, Dumbrovský M, Drbal K, ek Pavlík F, & Hlavinka P, (2016). Assessing the combined hazards of drought, soil erosion and local flooding on agricultural land: a Czech case study. Clim Res, 70(2-3), 231-249

  • van Wilgen NJ, Goodall V, Holness S, Chown SL, McGeoch MA (2016) Rising temperatures and changing rainfall patterns in South Africa’s national parks. Int J Climatol 36(2):706–721

    Article  Google Scholar 

  • Vicente-Serrano SM (2007) Evaluating the impact of drought using remote sensing in a mediterranean, semi-arid region. Nat Hazards 40(1):173–208

    Article  Google Scholar 

  • Wang Q, Yuan Z, Du Q, Li X (2018a) GETNET: A general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Trans Geosci Remote Sens 57(1):3–13

    Article  Google Scholar 

  • Wang Y, Meng B, Zhong S, Wang D, Ma J, Sun W (2018b) Aboveground biomass and root/shoot ratio regulated drought susceptibility of ecosystem carbon exchange in a meadow steppe. Plant Soil 432(1):259–272

    Article  Google Scholar 

  • Wild M. (2012) Solar radiation versus climate change. In: Meyers R.A. (ed.) Encyclopedia of sustainability science and technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0851-3_448

  • Williams A, de Vries FT (2020) Plant root exudation under drought: implications for ecosystem functioning. New Phytol 225(5):1899–1905

    Article  Google Scholar 

  • Workman CL, Ureksoy H (2017) Water insecurity in a syndemic context: understanding the psycho-emotional stress of water insecurity in Lesotho, Africa. Soc Sci Med 179:52–60

    Article  Google Scholar 

  • Yu M, Cho Y, Kim TW, Chae HS (2018) Analysis of drought propagation using hydrometeorological data: from meteorological drought to agricultural drought. J Korea Water Resour as 51(3):195–205

    Google Scholar 

  • Zhu Q, Luo Y, Zhou D, Xu YP, Wang G, Tian Y (2021) Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin. China Natural Hazards 105(2):2161–2185

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre for Environmental Management, University of the Free State, Bloemfontein, South Africa

    Israel R. Orimoloye & Olusola O. Ololade

  2. Disaster Management Training and Education Centre for Africa, University of the Free State, Bloemfontein, South Africa

    Israel R. Orimoloye & Johanes A. Belle

  3. Department of Geography and Environmental Science, Eastern Cape Province, University of Fort Hare, Private Bag X1314, Alice, 5700, South Africa

    Israel R. Orimoloye

  4. Department of Geography, University of the Free State, Bloemfontein, South Africa

    Adeyemi O. Olusola

  5. Department of Geography, University of Ibadan, Ibadan, Nigeria

    Adeyemi O. Olusola

  6. Sant Gadge Baba Amravati University and Dr. PDKV Akola, Amravati, India

    Chaitanya B. Pande

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  1. Israel R. Orimoloye
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  2. Adeyemi O. Olusola
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  3. Johanes A. Belle
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Orimoloye, I.R., Olusola, A.O., Belle, J.A. et al. Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms. Nat Hazards 112, 1085–1106 (2022). https://doi.org/10.1007/s11069-022-05219-9

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