Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis

Fooladi, Mahmood; Golmohammadi, Mohammad H.; Safavi, Hamid R.; Singh, Vijay P.

doi:10.1016/j.jenvman.2021.113283

Cited by 18 publications

(6 citation statements)

References 74 publications

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“…They combine with diverse meteorological (or hydrological) droughts and lead to agricultural consequences. Factors such as precipitation deficits, variations in the actual evapotranspiration capacity (evaporation from the soil and different surfaces and transpiration from plants), soil water deficits, and decreased water availability facilitate the detection and tracking of agricultural droughts [12][13][14].…”

Section: Agricultural Droughtmentioning

confidence: 99%

A Contemporary Review on Deep Learning Models for Drought Prediction

et al. 2023

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Deep learning models have been widely used in various applications, such as image and speech recognition, natural language processing, and recently, in the field of drought forecasting/prediction. These models have proven to be effective in handling large and complex datasets, and in automatically extracting relevant features for forecasting. The use of deep learning models in drought forecasting can provide more accurate and timely predictions, which are crucial for the mitigation of drought-related impacts such as crop failure, water shortages, and economic losses. This review provides information on the type of droughts and their information systems. A comparative analysis of deep learning models, related technology, and research tabulation is provided. The review has identified algorithms that are more pertinent than others in the current scenario, such as the Deep Neural Network, Multi-Layer Perceptron, Convolutional Neural Networks, and combination of hybrid models. The paper also discusses the common issues for deep learning models for drought forecasting and the current open challenges. In conclusion, deep learning models offer a powerful tool for drought forecasting, which can significantly improve our understanding of drought dynamics and our ability to predict and mitigate its impacts. However, it is important to note that the success of these models is highly dependent on the availability and quality of data, as well as the specific characteristics of the drought event.

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Section: Agricultural Droughtmentioning

confidence: 99%

A Contemporary Review on Deep Learning Models for Drought Prediction

et al. 2023

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“…The percentage of dominant land cover types at each grid was selected as ling factor to analyze the aggregation characteristics of the underlying surfa means cluster method is an unsupervised machine learning algorithm that is w for clustering problems [50][51][52] because it is an easy application to use and ha ciency and excellent performance [53]. The K-means cluster splits numerous d into K clusters and K coherent centers based on specific characteristics [54], wh the lengths of the internal points among each cluster to be small as possible, wh the lengths between clusters to be as large as possible. K-means clustering is co minimizing the objective function (Equations ( 1) and ( 2)), and the aim is that the square error, S, is the minimum value.…”

Section: K-means Cluster Analysismentioning

confidence: 99%

“…K-means clustering is controlled by minimizing the objective function (Equations ( 1) and ( 2)), and the aim is that the sum of the square error, S, is the minimum value. For the specific calculation steps for the K-means cluster, see the references [54][55][56].…”

Section: K-means Cluster Analysismentioning

confidence: 99%

Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

Zhang

Yin

et al. 2022

Remote Sensing

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The characteristics of the drought propagation from meteorological drought (MD) to agricultural drought (AD) differ in various climatic and underlying surface conditions. However, how these factors affect the process of drought propagation is still unclear. In this study, drought propagation and influencing factors were investigated in an arid region of Northeast Asia (ARNA) during 1982–2014. Based on run theory, the drought characteristics were detected using the standardized precipitation index (SPI) and standardized soil moisture index (SMI), respectively. The propagation time from MD to AD was investigated, and the influence factors were identified. Results demonstrated that five clusters (C1–C5) based on land cover distribution were further classified by the K-means cluster algorithm to discuss the spatial and seasonal propagation variation. MD and AD in ARNA became more severe during the study period in all five clusters. The propagation times from MD to AD in all five clusters were shorter (1–3 months) in summer and autumn and longer (5–12 months) in spring and winter. This result suggested that the impact of vegetation on the seasonal drought propagation time was more obvious than that of the spatial drought propagation time. Precipitation and vegetation were the major impactors of AD in spring, summer and autumn (p < 0.05). The impact of precipitation on AD was more noticeable in summer, while vegetation mainly influenced AD in spring and autumn. The research also found that drought propagation time had a negative relationship (p < 0.05) with precipitation, evapotranspiration, soil moisture and NDVI in this region, which indicated that a rapid hydrological cycle and vegetation can shorten the propagation time from MD to AD. This study can help researchers to understand the drought propagation process and the driving factors to enhance the efficiency of drought forecasting.

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“…So, a new strategy called the combination/fusion-based method has been proposed to overcome the downsides of IML models. This idea can forecast features with higher accuracy than individual data-driven models (Fooladi et al 2021;Dasarathy 1997). The errors in IML are independent of statistical property.…”

Section: Introductionmentioning

confidence: 99%

A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran

Khajehali,

Safavi,

Nikoo

et al. 2024

Nat Hazards

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This study proposes a novel fusion framework for ood forecasting based on machine learning, statistical, and geostatistical models for daily multiple-step-ahead and near future under climate change scenarios. To do this, remote sensing precipitation data of ERA5, CHIRPS, and PERSIANN-CDR were utilized to ll the gap data of meteorological stations. Four Individual Machine Learning (IML) models, including Random Forest, Multiple-Layer Perceptron, Support Vector Machine, and Extreme Learning Machine were developed for twelve days ahead of stream ow modeling. Then, three fusion models, including Random Forest, Bayesian Model Averaging, and Bayesian Maximum Entropy were applied to combine the outputs of IML models. The proposed framework also was implemented to downscale the precipitation variable of three general climate models (GCMs) under SSP5-8.5 and SSP1-2.6 scenarios. The results indicated that individual models illustrated weak performance, especially in far steps ood forecasting, so it is necessary to utilize a fusion technique to improve the results. In the fusion step, the RF model indicated high e ciency compared to other fusion models. This technique also demonstrated an effective pro ciency in downscaling precipitation data of GCMs on a daily scale. Finally, ood forecasting model was developed based on the fusion framework in the near future (2020-2040) by using the precipitation data of two scenarios. We conclude that ood events based on both SSP5-8.5 and SSP1-2.6 will increase in the future in our case study. Also, the frequency evaluation shows that oods under SSP1-2.6 will occur about 10 percent more than SSP5-8.5 in the Kan river basin from 2020 to 2040.

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Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis

Cited by 18 publications

References 74 publications

A Contemporary Review on Deep Learning Models for Drought Prediction

A Contemporary Review on Deep Learning Models for Drought Prediction

Evaluation of Drought Propagation Characteristics and Influencing Factors in an Arid Region of Northeast Asia (ARNA)

A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran

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