Estimation of prediction interval in ANN-based multi-GCMs downscaling of hydro-climatologic parameters

Nourani, Vahid; Paknezhad, Nardin Jabbarian; Sharghi, Elnaz; Khosravi, Abbas

doi:10.1016/j.jhydrol.2019.124226

Cited by 49 publications

(17 citation statements)

References 58 publications

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“…Moreover, it was concluded that the LUBE method was efficient in quantifying the uncertainty of data-driven models. Nourani, Paknezhad, Sharghi and Khosravi [57] used the LUBE method to construct the PIs associated with the ANN-based downscaling of the general circulation models. In this study, the LUBE method was applied by generating multiple sets of weights to develop narrow PIs with high coverage probability.…”

Section: Lube Methodsmentioning

confidence: 99%

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

2021

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Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology.

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Section: Lube Methodsmentioning

confidence: 99%

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

2021

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“…These two data were used to train the ANN. Further details about training an ANN for downscaling can be found in Abrahart et al, 2004;Nourani et al, 2019;Vu et al, 2016 [28-30]. Before analysis, our data were quality-controlled.…”

Section: Meteorological Datamentioning

confidence: 99%

“…The ANN is a network of interconnected neurons, a computing system created to process information like the human brain [28,29] (Supplementary Material Figure S3). ANN is increasingly preferred [13,30,39,40] because it is inexpensive but efficient [13,41].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The optimal set of weights is determined through the learning process, which consists of training ANN with known input and output values. The three-layer FNN-BP network model in this study was trained using the Levenberg-Marquardt optimization algorithm [29,40]. In this study, we used the sigmoid and linear transfer function in the hidden and output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Assessment of the Future Impact of Climate Change on the Hydrology of the Mangoky River, Madagascar Using ANN and SWAT

Tanteliniaina

Rahaman²,

Zhai

2021

Water

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The assessment of the impacts of climate change on hydrology is important for better water resources management. However, few studies have been conducted in semi-arid Africa, even less in Madagascar. Here we report, climate-induced future hydrological prediction in Mangoky river, Madagascar using an artificial neural network (ANN) and the soil and water assessment tool (SWAT). The current study downscaled two global climate models on the mid-term, noted the 2040s (2041–2050) and long-term, noted 2090s (2091–2099) under two shared socioeconomic pathways (SSP) scenarios, SSP 3–7.0 and SSP 5–8.5. Statistical indices of both ANN and SWAT showed good performance (R2 > 0.65) of the models. Our results revealed a rise in maximum temperature (4.26–4.69 °C) and minimum temperature (2.74–3.01 °C) in the 2040s and 2090s. Under SSP 3–7.0 and SSP 5–8.5, a decline in the annual precipitation is projected in the 2040s and increased the 2090s. This study found that future precipitation and temperature could significantly decrease annual runoff by 60.59% and 73.77% in the 2040s; and 25.18% and 23.45% in the 2090s under SSP 3–7.0 and SSP 5–8.5, respectively. Our findings could be useful for the adaptation to climate change, managing water resources, and water engineering.

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“…However, scale discrepancy limits the coarse resolution data sets from being directly used for impact assessments and decision making. One solution for bridging this gap is to downscale coarse resolution data to the local scale (Chen et al., 2010; Luo et al., 2018; Nourani et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Daily Precipitation and Temperature Downscaling

Fang

Tian

Lowe

et al. 2021

Water Resources Research

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Understanding the impact of climate variability and change is of great importance for developing adaptation and mitigation strategies. Coarse resolution data sets (such as climate reanalysis, numerical weather predictions, satellite products, and simulations of general circulation models) are important for reconstructing historical climate and predicting the future. However, scale discrepancy limits the coarse resolution data sets from being directly used for impact assessments and decision making. One solution for bridging this gap is to downscale coarse resolution data to the local scale (

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Estimation of prediction interval in ANN-based multi-GCMs downscaling of hydro-climatologic parameters

Cited by 49 publications

References 58 publications

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

Assessment of the Future Impact of Climate Change on the Hydrology of the Mangoky River, Madagascar Using ANN and SWAT

Deep Learning for Daily Precipitation and Temperature Downscaling

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