Meteorological drought assessment in northern Bangladesh: A machine learning-based approach considering remote sensing indices

Sadiq, Md. Ashhab; Sarkar, Showmitra Kumar; Raisa, Saima Sekander

doi:10.1016/j.ecolind.2023.111233

Cited by 8 publications

(2 citation statements)

References 62 publications

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“…Such case studies have raised questions about operative and accurate methods for data processing aimed at flood prediction and management. Accurate classification of satellite images for environmental mapping requires advanced methods and scripting languages that aim at robust detection of patterns [34][35][36][37][38]. Existing state-of-the-art methods of image processing and classification mostly use traditional GIS for data handling, which may lead to misclassification and inaccurate labelling of pixels.…”

Section: Gap and Motivationmentioning

confidence: 99%

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Lemenkova

2024

Water

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Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.

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Section: Gap and Motivationmentioning

confidence: 99%

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Lemenkova

2024

Water

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“…Drought monitoring using vegetation indices such as VHI or NDVI (Normalized difference vegetation index) or VCI (Vegetation condition index) has been developed in several locations using satellite imagery from products such as MODIS, and NOAA STAR (Sadiq et al, 2023;Kloos et al, 2021). Machine learning has been used to forecast vegetation indices at timescales including daily, 5 and 7 day intervals (Kartal et al, 2024;Kladny et al, 2024;Reddy and Prasad, 2018), monthly intervals (Lees et al, 2022), weekly timescales (Barrett et al, 2020), and average vegetation condition values aggregated over 1-3 months (Adede et al, 2019).…”

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confidence: 99%

Application of machine learning to forecast agricultural drought impacts for large scale sub-seasonal drought monitoring in Brazil

Gallear,

Valadares Galdos,

Zeri

et al. 2024

Preprint

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Abstract. Drought events have increased in frequency and severity in recent years, and result in significant economic losses. Although the Brazilian semi-arid northeast has been historically associated with the impacts of drought, drought is of national concern, from 2011–2019, drought events were recorded in all Brazilian territories. Droughts can have major consequences for agricultural production, which is of particular concern given the importance of soybeans for socio-economic development. Due to its regional heterogeneity, it is important to develop accurate drought forecast and assessment tools for Brazil. We explore machine learning as a method to forecast the vegetation health index (VHI), for large scale monthly drought monitoring across agricultural land in Brazil. Furthermore, we also determine spatio-temporal drivers of VHI across the wide variation in climates, as well as evaluate machine learning performance for ENSO variation, forecasting of the onset of drought impact, and how the trade off between spatial variation and sample size affects model performance. We show that machine learning methods such as gradient boosting methods are able to more easily forecast vegetation health in the north and north east Brazil than south Brazil, and perform better during La Niña events than El Niño events. Drought impacts which reduce VHI below the commonly used 40 % threshold can be forecast across Brazil with similar model performance. SPEI is shown to be a useful indicator of drought impact, with 3 month accumulation periods preferred over 1 and 2 months. Results aim to inform future developments in operational drought monitoring at the National Center for Monitoring and Early Warning of Natural Disasters in Brazil (CEMADEN). Future work should build upon methods discussed here to improve drought forecasts for agricultural drought response and adaptation.

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Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China

Wang,

Lin,

et al. 2025

Agricultural Water Management

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Meteorological drought assessment in northern Bangladesh: A machine learning-based approach considering remote sensing indices

Cited by 8 publications

References 62 publications

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Application of machine learning to forecast agricultural drought impacts for large scale sub-seasonal drought monitoring in Brazil

Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China

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