Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach

Asfaw, Wegayehu; Rientjes, T.H.M.; Haile, Alemseged Tamiru

doi:10.1016/j.ejrh.2022.101287

Cited by 3 publications

(1 citation statement)

References 89 publications

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“…In the field of hydrology, BMA is often used for improving the prediction and reducing the uncertainty of analyses [28]. For example, Asfaw et al [34] used BMA to fuse four high-resolution satellite rainfall estimation (SRE) products to improve the estimation of rainfall. The results showed that the BMA model significantly reduced the error in estimating rainfall.…”

Section: Introductionmentioning

confidence: 99%

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Chu,

Song,

Chen

2024

Water

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Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction.

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Section: Introductionmentioning

confidence: 99%

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Chu,

Song,

Chen

2024

Water

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Integrating an interpolation technique and AI models using Bayesian model averaging to enhance groundwater level monitoring

Wang,

Wang

2024

Earth Sci Inform

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The Use of Agricultural Databases for Crop Modeling: A Scoping Review

Mthembu,

Kunz,

Gokool

et al. 2024

Sustainability

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There is growing interest in promoting neglected and underutilized crop species to enhance agrobiodiversity and contribute to food systems transformation under climate change. A lack of available measured data has hindered the mainstreaming of these crops and limited the ability of agricultural databases to be used for calibrating and validating crop models. This study conducts a systematic scoping review and bibliometric analysis to assess the use of agricultural databases for crop modeling. The Biblioshiny App v4.1.2 and VOSviewer software v1.6.20 were used to analyze 51 peer-reviewed articles from Scopus and Web of Science. Key findings from this review were that agricultural databases have been used for estimating crop yield, assessing soil conditions, and fertilizer management and are invaluable for developing decision support tools. The main challenges include the need for high-quality datasets for developing agricultural databases and more expertise and financial resources to develop and apply crop and machine learning models. From the bibliometric dataset, only one study used modeled data to develop a crop database despite such data having a level of uncertainty. This presents an opportunity for future research to improve models to minimize their uncertainty level and provide reliable data for crop database development.

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Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach

Cited by 3 publications

References 89 publications

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Integrating an interpolation technique and AI models using Bayesian model averaging to enhance groundwater level monitoring

The Use of Agricultural Databases for Crop Modeling: A Scoping Review

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