2020
DOI: 10.1029/2019ms001909
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Optimization of Deep Learning Precipitation Models Using Categorical Binary Metrics

Abstract: This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection and false alarm rate are popular metrics used in the verification of precipitation models. However, machine learning models trained using gradient descent cannot be optimized based on these metrics, as they are not differentiable. We propose an alternative formulation for these categorical indices that are … Show more

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Cited by 17 publications
(20 citation statements)
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“…The ability of AI to process enormous amounts of non-structured, multi-dimensional data using sophisticated optimisation techniques is already facilitating the understanding of high-dimensional climate datasets and forecasting of future trends (Huntingford et al 2019 ). AI techniques have been used to forecast global mean temperature changes (Ise and Oba 2019 ; Cifuentes et al 2020 ); predict climactic and oceanic phenomena such as El Niño (Ham et al 2019 ), cloud systems (Rasp et al 2018 ), and tropical instability waves (Zheng et al 2020 ); better understand aspects of the weather system—like rainfall, generally (Sønderby et al 2020 ; Larraondo et al 2020 ) and in specific locales, such as Malaysia (Ridwan et al 2020 )—and their knock-on consequences, like water demand (Shrestha et al 2020 ; Xenochristou et al 2020 ). AI tools can also help anticipate the extreme weather events that are more common as a result of global climate change, for example heavy rain damage (Choi et al 2018 ) and wildfires (Jaafari et al 2019 ), and other downstream consequences, such as patterns of human migration (Robinson and Dilkina 2018 ).…”
Section: Ai Against Climate Changementioning
confidence: 99%
“…The ability of AI to process enormous amounts of non-structured, multi-dimensional data using sophisticated optimisation techniques is already facilitating the understanding of high-dimensional climate datasets and forecasting of future trends (Huntingford et al 2019 ). AI techniques have been used to forecast global mean temperature changes (Ise and Oba 2019 ; Cifuentes et al 2020 ); predict climactic and oceanic phenomena such as El Niño (Ham et al 2019 ), cloud systems (Rasp et al 2018 ), and tropical instability waves (Zheng et al 2020 ); better understand aspects of the weather system—like rainfall, generally (Sønderby et al 2020 ; Larraondo et al 2020 ) and in specific locales, such as Malaysia (Ridwan et al 2020 )—and their knock-on consequences, like water demand (Shrestha et al 2020 ; Xenochristou et al 2020 ). AI tools can also help anticipate the extreme weather events that are more common as a result of global climate change, for example heavy rain damage (Choi et al 2018 ) and wildfires (Jaafari et al 2019 ), and other downstream consequences, such as patterns of human migration (Robinson and Dilkina 2018 ).…”
Section: Ai Against Climate Changementioning
confidence: 99%
“…The ability of AI to process enormous amounts of non-structured, multi-dimensional data using sophisticated optimisation techniques is already facilitating the understanding of high-dimensional climate datasets and forecasting of future trends (Huntingford et al 2019). AI techniques have been used to forecast global mean temperature changes (Ise and Oba 2019;Cifuentes et al 2020); predict climactic and oceanic phenomena such as El Niño (Ham, Kim, and Luo 2019), cloud systems (Rasp, Pritchard, and Gentine 2018), and tropical instability waves (Zheng et al 2020); better understand aspects of the weather system -like rainfall, generally (Sønderby et al 2020;Larraondo et al 2020) and in specific locales, such as Malaysia (Ridwan et al 2020) -and their knock-on consequences, like water demand (Shrestha, Manandhar, and Shrestha 2020;Xenochristou et al 2020).…”
Section: How Ai Is Used Against Climate Changementioning
confidence: 99%
“…for which means element-wise multiplication (P. R. Larraondo et al, 2020). For logical operations like observed < α ð Þor observed > α ð Þ , it is 1 if the statement is true, otherwise it is 0.…”
Section: Model Evaluationmentioning
confidence: 99%
“…These metrics are noncontinuous, non-differentiable, and unsuitable for optimizing deep learning models. P. R. Larraondo et al (2020) proposed an alternative formulation for these categorical binary indices, which are defined as follows:…”
Section: Model Evaluationmentioning
confidence: 99%
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