IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v112y2022i2d10.1007_s11069-022-05219-9.html
   My bibliography  Save this article

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

Author

Listed:
  • Israel R. Orimoloye

    (University of the Free State
    University of the Free State
    University of Fort Hare)

  • Adeyemi O. Olusola

    (University of the Free State
    University of Ibadan)

  • Johanes A. Belle

    (University of the Free State)

  • Chaitanya B. Pande

    (Sant Gadge Baba Amravati University and Dr. PDKV Akola)

  • Olusola O. Ololade

    (University of the Free State)

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.

Suggested Citation

  • Israel R. Orimoloye & Adeyemi O. Olusola & Johanes A. Belle & Chaitanya B. Pande & Olusola O. Ololade, 2022. "Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1085-1106, June.
    • Handle: RePEc:spr:nathaz:v:112:y:2022:i:2:d:10.1007_s11069-022-05219-9
    DOI: 10.1007/s11069-022-05219-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05219-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-022-05219-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Workman, Cassandra L. & Ureksoy, Heather, 2017. "Water insecurity in a syndemic context: Understanding the psycho-emotional stress of water insecurity in Lesotho, Africa," Social Science & Medicine, Elsevier, vol. 179(C), pages 52-60.
    2. Michael Carter & Peter Little & Tewodaj Mogues & Workneh Negatu, 2005. "Shocks, Sensitivity and Resilience: Tracking the Economic Impacts of Environmental Disaster on Assets in Ethiopia and Honduras," Development and Comp Systems 0511029, University Library of Munich, Germany.
    3. Benjamin Kipkemboi Kogo & Lalit Kumar & Richard Koech, 2021. "Climate change and variability in Kenya: a review of impacts on agriculture and food security," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 23-43, January.
    4. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    5. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    6. Feng, Puyu & Wang, Bin & Liu, De Li & Yu, Qiang, 2019. "Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia," Agricultural Systems, Elsevier, vol. 173(C), pages 303-316.
    7. Jose A. Marengo & Ana Paula M. A. Cunha & Carlos A. Nobre & Germano G. Ribeiro Neto & Antonio R. Magalhaes & Roger R. Torres & Gilvan Sampaio & Felipe Alexandre & Lincoln M. Alves & Luz A. Cuartas & K, 2020. "Assessing drought in the drylands of northeast Brazil under regional warming exceeding 4 °C," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 2589-2611, September.
    8. Sergio Vicente-Serrano, 2007. "Evaluating the Impact of Drought Using Remote Sensing in a Mediterranean, Semi-arid Region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 40(1), pages 173-208, January.
    9. Qian Zhu & Yulin Luo & Dongyang Zhou & Yue-Ping Xu & Guoqing Wang & Ye Tian, 2021. "Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 2161-2185, January.
    10. Irina Mahlstein & John S. Daniel & Susan Solomon, 2013. "Pace of shifts in climate regions increases with global temperature," Nature Climate Change, Nature, vol. 3(8), pages 739-743, August.
    11. Jenq-Tzong Shiau & Jia-Wei Lin, 2016. "Clustering Quantile Regression-Based Drought Trends in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1053-1069, February.
    12. Dai, Meng & Huang, Shengzhi & Huang, Qiang & Leng, Guoyong & Guo, Yi & Wang, Lu & Fang, Wei & Li, Pei & Zheng, Xudong, 2020. "Assessing agricultural drought risk and its dynamic evolution characteristics," Agricultural Water Management, Elsevier, vol. 231(C).
    13. Siri Aas Rustad & Elisabeth Lio Rosvold & Halvard Buhaug, 2020. "Development Aid, Drought, and Coping Capacity," Journal of Development Studies, Taylor & Francis Journals, vol. 56(8), pages 1578-1593, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vikram Bharti & Thendiyath Roshni & Madan Kumar Jha & Mohammad Ali Ghorbani & Osama Ragab Abdelaziz Ibrahim, 2024. "Complex network analysis of groundwater level in Sina Basin, Maharashtra, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 18017-18032, July.
    2. Chaitanya B. Pande & Nadhir Al-Ansari & N. L. Kushwaha & Aman Srivastava & Rabeea Noor & Manish Kumar & Kanak N. Moharir & Ahmed Elbeltagi, 2022. "Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree," Land, MDPI, vol. 11(11), pages 1-24, November.
    3. Ramón Espinel & Gricelda Herrera-Franco & José Luis Rivadeneira García & Paulo Escandón-Panchana, 2024. "Artificial Intelligence in Agricultural Mapping: A Review," Agriculture, MDPI, vol. 14(7), pages 1-36, July.
    4. Fatemeh Firoozi & Ahmad Fakheri Fard & Esmaeil Asadi, 2024. "Detection and Attribution of Meteorological Drought to Anthropogenic Climate Change (Case Study: Ajichay basin, Iran)," Climatic Change, Springer, vol. 177(8), pages 1-25, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Hao & Yang, Ni & Song, Xuanhua & Lu, Chunhua & Lu, Menglan & Chen, Tan & Deng, Shulin, 2025. "A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning," Agricultural Water Management, Elsevier, vol. 308(C).
    2. Paweł Teisseyre & Robert A. Kłopotek & Jan Mielniczuk, 2016. "Random Subspace Method for high-dimensional regression with the R package regRSM," Computational Statistics, Springer, vol. 31(3), pages 943-972, September.
    3. Xiao, Xin & Ming, Wenting & Luo, Xuan & Yang, Luyi & Li, Meng & Yang, Pengwu & Ji, Xuan & Li, Yungang, 2024. "Leveraging multisource data for accurate agricultural drought monitoring: A hybrid deep learning model," Agricultural Water Management, Elsevier, vol. 293(C).
    4. Cem Polat Cetinkaya & Mert Can Gunacti, 2024. "Meteorological and Agricultural Drought Risk Assessment via Kaplan–Meier Survivability Estimator," Agriculture, MDPI, vol. 14(3), pages 1-15, March.
    5. Samuel Asumadu Sarkodie & Maruf Yakubu Ahmed & Phebe Asantewaa Owusu, 2022. "Global adaptation readiness and income mitigate sectoral climate change vulnerabilities," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-17, December.
    6. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    7. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    8. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    9. Kebede, Sindu & Fekadu, Belay & Aredo, Dejene, 2011. "Trade Liberalization and Poverty: A Macro-Micro Analysis in Ethiopia," Proceedings of the German Development Economics Conference, Berlin 2011 44, Verein für Socialpolitik, Research Committee Development Economics.
    10. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    11. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    12. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    13. Rui Li & Jing’ai Wang & Tianjie Zhao & Jiancheng Shi, 2016. "Index-based evaluation of vegetation response to meteorological drought in Northern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2179-2193, December.
    14. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    16. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    17. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    18. Franklin Paredes-Trejo & Humberto Alves Barbosa & Gabriel Antunes Daldegan & Ingrid Teich & César Luis García & T. V. Lakshmi Kumar & Catarina de Oliveira Buriti, 2023. "Impact of Drought on Land Productivity and Degradation in the Brazilian Semiarid Region," Land, MDPI, vol. 12(5), pages 1-19, April.
    19. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    20. Siyu Han & Shixiang Yu & Mengya Shi & Makoto Harada & Jianhong Ge & Jiesheng Lin & Cornelia Prehn & Agnese Petrera & Ying Li & Flora Sam & Giuseppe Matullo & Jerzy Adamski & Karsten Suhre & Christian , 2025. "LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer," Nature Communications, Nature, vol. 16(1), pages 1-20, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:112:y:2022:i:2:d:10.1007_s11069-022-05219-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.