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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
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    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.
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    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.

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