Machine learning and geographic information systems-based framework for multidimensional analysis of cascading drought impacts using remote sensing and in-situ data

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dc.contributor.authorSerkendiz, Hıdır
dc.contributor.authorTatli, Hasan
dc.contributor.authorÖzelkan, Emre
dc.contributor.authorÇetin, Mahmut
dc.date.accessioned2026-02-03T11:53:41Z
dc.date.available2026-02-03T11:53:41Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThis study proposes a multidimensional conceptual framework to assess the cascading impacts of drought on the agricultural sector. The framework consists of four interconnected components: the triggering hazard, biophysical drivers, socio-ecological impacts, and socio-economic outcomes. To demonstrate its applicability, the framework was applied to the Konya Closed Basin, a drought-sensitive agricultural region in central Türkiye. The study integrates remote sensing indicators (NDVI, NDWI, LST, and land cover), ground-based observations (precipitation, temperature, groundwater levels), and statistical trend analyses (Mann-Kendall) to characterize drought dynamics and land use transitions. Machine learning algorithms were used to model land use change: drought-related indicators such as NDVI, NDWI, LST, and Palmer Drought Severity Index (PDSI) served as input variables, while Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forests (RF) were applied as classification tools to predict land cover changes over time. Between 1990 and 2018, approximately 510,000 ha of irrigated land, including rice fields, were converted into non-irrigated areas. Despite this trend, the production of water-intensive crops such as maize and sugar beet continued to rise, indicating a maladaptive trajectory in agricultural practices. This mismatch between environmental constraints and production patterns highlights unsustainable water use and signals potential long-term risks to both water and food security. The proposed framework not only enhances understanding of cascading drought impacts but also offers critical insights for adaptive agricultural and water governance, supporting evidence-based policymaking in climate-vulnerable regions. © 2025 Elsevier B.V.
dc.identifier.doi10.1016/j.scitotenv.2025.180504
dc.identifier.issn0048-9697
dc.identifier.pmid40961611
dc.identifier.scopus2-s2.0-105015804615
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.scitotenv.2025.180504
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34288
dc.identifier.volume1001
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofScience of the Total Environment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260130
dc.subjectAgricultural production
dc.subjectCascading Hazard
dc.subjectCascading risk
dc.subjectChange detection
dc.subjectDrought
dc.subjectGroundwater
dc.subjectLand use change
dc.subjectMachine learning
dc.titleMachine learning and geographic information systems-based framework for multidimensional analysis of cascading drought impacts using remote sensing and in-situ data
dc.typeArticle

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