Streamflow and rainfall forecasting by two long short-term memory-based models

Ni, Lingling; Wang, Dong; Singh, Vijay P.; Wu, Jianfeng; Wang, Yuankun; Tao, Yuwei; Zhang, Jianyun

doi:10.1016/j.jhydrol.2019.124296

“…However, these studies have not emphasized the inclusion of physical watershed characteristics in the models. Recent studies (Chu et al, 2020;Kratzert et al, 2018;Ni et al, 2020; have applied deep learning models to multiple watersheds while using a separate model for each watershed or gauge. While developing one model for each watershed improves accuracy of prediction, it is inefficient and difficult to apply on a larger scale.…”

Section: Introductionmentioning

confidence: 99%

A Regional Semi-Distributed Streamflow Model Using Deep Learning

Xiang

¹

,

Demir

²

,

Mantilla

³

et al. 2021

Preprint

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Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…), the ability of mentioned models (i.e., ANN, SARIMA, and ES) to forecast highly nonstationary and seasonal hydrological time series (e.g., streamflow) may be restricted due to the multifrequency nature of the real hydrological process. To handle the mentioned nonstationary problem, the application of wavelet-based data preprocessing has been already proposed and used successfully in hydro-environmental modeling [26][27][28][29]. For example, Jamei et al [27] developed waveletmultigene genetic programming for the simulation of surface water total dissolved solids.…”

Section: Introductionmentioning

confidence: 99%

“…Ahmadianfar et al [26] developed the locally weighted linear regression with the wavelet transform (WT) for the prediction of electrical conductivity in surface water. Ni et al [29] used a hybrid wavelet and long short-term memory (LSTM) network for monthly streamflow and rainfall forecasting and indicated the superiority of the hybrid model against conventional LSTM.…”

Section: Introductionmentioning

confidence: 99%

Using Hybrid Wavelet‐Exponential Smoothing Approach for Streamflow Modeling

Nourani

¹

,

Najafi

²

,

Amini

³

et al. 2021

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Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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“…Son yıllarda, beynin yapısından ve işlevinden ilham alan yapay sinir ağları tabanlı algoritmalarla ilgili bir makine öğrenmesinin alt alanı olan Derin öğrenme yaklaşımı tahmin çalışmalarında kullanılmaktadır. [16][17][18]. Derin öğrenme, çok katmanlı modellerle yapılan yapay öğrenme olup, Yapay Sinir Ağlarındaki gizli katmanın sayısının artırılmış ve geliştirilmiş halidir.…”

Section: Introductionunclassified

“…LSTM mimarisinin RNN mimarisine göre en temel avantajları; sıfırlanan gradyan problemine çözüm oluşturabilmesi ve girdilerin unutulmadan saklanabilmesi aracılığıyla bilgi kaybını engelleyebilmesidir. Bu nedenle LSTM mimarisi yaygın olarak kullanılmaya başlanmıştır [20].…”

Section: Introductionunclassified

Tekil Spektrum Analizi ve Uzun-Kısa Süreli Bellek Ağları ile Nehir Akım Tahmini

Latifoğlu

¹

,

NURALAN

²

2020

European Journal of Science and Technology

Get access via publisher Add to dashboard Cite

Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.

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Streamflow and rainfall forecasting by two long short-term memory-based models

Cited by 302 publications

References 48 publications

A Regional Semi-Distributed Streamflow Model Using Deep Learning

A Regional Semi-Distributed Streamflow Model Using Deep Learning

Using Hybrid Wavelet‐Exponential Smoothing Approach for Streamflow Modeling

Tekil Spektrum Analizi ve Uzun-Kısa Süreli Bellek Ağları ile Nehir Akım Tahmini

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