Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms

Kafy, Abdulla Al; Bakshi, Arpita; Saha, Milan; Faisal, Abdullah-Al-; Almulhim, Abdulaziz I.; Rahaman, Zullyadini A.; Mohammad, Pir

doi:10.1016/j.scitotenv.2023.161394

Cited by 52 publications

(9 citation statements)

References 60 publications

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“…This involved comparing the results of the agricultural drought vulnerability mapping with a ground-truth dataset that represents the actual drought occurrences in your study area. The SM data may be used to validate the agricultural drought vulnerability map because it is a crucial indicator of agricultural droughts (Kafy et al, 2023). Several studies (Hoque et al, 2021) mentioned SM data as a validation indicator for agricultural drought vulnerability.…”

Section: Accuracy Assessment For Drought Risk Mappingmentioning

confidence: 99%

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

Islam¹,

Yeșilköy²,

Baydaroğlu³

et al. 2023

Preprint

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Due to the shifting climate, extreme events are being observed more frequently globally. Drought is one of the most common natural hazards that severely impacts communities in terms of economic losses and agricultural production disruption. Considering global trade, drought in an agricultural region affects the food security in other regions because of disrupted supply. Decision-makers often consult susceptibility maps when preparing mitigation plans so that the adverse impacts of a drought event can be reduced. Creating drought susceptibility maps can be demanding, requiring a lot of data (i.e., hydrological and land use), expertise, and thorough assessment to accurately picture a vulnerable region’s condition. The process also relies on complex hydrological and hydrometeorological models. The objective of this investigation is to examine the vulnerability and impact of drought and formulate maps of drought susceptibility, exposure, and risk by considering a multitude of atmospheric, physical and social indicators. Subsequent to this notion, a fuzzy logic algorithm has been devised by assigning a comprehensive array of weights to each parameter derived from an exhaustive literature review and used for a preliminary investigation for the state of Iowa. This state is located in the Corn Belt region, and its primary economic activity is agriculture. Drought susceptibility maps for the state of Iowa have been generated for the period spanning from 2015 to 2021 and validated using the Kappa coefficient. The produced drought susceptibility maps can support drought mitigation plans and decisions for communities in Iowa.

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Section: Accuracy Assessment For Drought Risk Mappingmentioning

confidence: 99%

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

Islam¹,

Yeșilköy²,

Baydaroğlu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These investigations display a range of methodological approaches, where certain studies lean on a singular drought index to delineate drought conditions [16][17][18], while others employ a multitude of indices for a more nuanced assessment [19][20][21]. Additionally, certain research delves into historical drought occurrences and the underlying causes [22][23][24], whereas others project future drought scenarios through methodologies such as the statistical downscaling of Global Climate Models (GCMs) [25][26][27], machine learning, AI techniques [28][29][30], etc. Furthermore, the impact assessments of drought extend across agricultural [31], ecological [32], and socioeconomic domains [7], reflecting the intricate interplay between water scarcity and diverse societal sectors.…”

Section: Introductionmentioning

confidence: 99%

A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises

Afsari,

Nazari-Sharabian,

Hosseini

et al. 2024

Water

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This study extensively explores the impact of climate change on meteorological droughts within metropolises in Iran. Focused on Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz, this research employed CMIP6 climate models under varying climate change scenarios (SSPs) to forecast severe meteorological droughts spanning the period from 2025 to 2100. The investigation utilized a diverse set of drought indices (SPI, DI, PN, CZI, MCZI, RAI, and ZSI) to assess the drought severity in each city. This study is crucial as it addresses the pressing concerns of rapidly decreasing water levels in Iran’s dams, serious declines in underground aquifers, and the compounding issues of land subsidence and soil erosion due to excessive groundwater withdrawal in the face of severe droughts. This study culminated in the generation of box plots and heatmaps based on the results. These visual representations elucidated the distribution of the drought values under different indices and scenarios and provided a depiction of the probability of severe drought occurrences until the end of the century for each city. The resulting findings serve as invaluable tools, furnishing policymakers with informed insights to proactively manage and fortify metropolitan resilience against the evolving challenges posed by a changing climate.

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“…Given the growing threat of drought caused by climate change, it is essential to be aware of past trends and weaknesses to develop effective measures for adapting to and mitigating its impacts (Gosh et al, 2024). The Ningxia Hui Autonomous Region in northwest China is susceptible to droughts due to its arid climate, elevated temperatures, and limited yearly rainfall (Kafy et al, 2023). This study examines the drought patterns in Ningxia from 2003 to 2023 by utilizing satellite remote sensing data and climatic records (Tyagi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

Awais,

Zaheen,

Fatima

et al. 2024

ABBDM

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Drought represents a significant disaster that directly impacts the economic and ecological welfare of any nation it afflicts. This study focused on the climatic anomalies of drought over the Ningxia Hui autonomous region in northwest China over the last two decades. The study employed an in-depth machine learning model, which incorporated drought indices, thus leading to a data-informed analysis of Ningxia drought patterns. The study accomplished this by using MODIS satellite data products available for vegetation and moisture monitoring. The MOD09GA, MOD11A2, and MCD43A4 data streams were loaded into Google Earth Engine as factors to develop a time-series dataset of vegetation indices. Indices are Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Temperature (LST) measurements are taken into account. Data on temperature, precipitation, and evapotranspiration was compiled for the period from 2003 to 2023 and calculated standardized indices on pixel level for the whole Ningxia region to develop the Standardized Precipitation Index (SPI), Keetch-Byram Drought Index (KBDI), and Standardized Precipitation-Evapotranspiration Index ( The study results indicated that SPI fell significantly from the year 2003 to 2023, from 0.7 to -0.3. The SPEI plummeted from 0.5 to -0.2 during the observed time frame. KBDI also went up, through 581.33 in 2003 and 681.091 in 2023, showing deterioration of aridity and drying of the soil. The conclusion of this study focuses on the deterioration of drought conditions in the Ningxia region in the last 20 years.

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Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms

Cited by 52 publications

References 60 publications

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises

Spatiotemporal Drought Assessment in Ningxia Autonomous Region: A Machine Learning and Remote Sensing Approach

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