Searched for: subject%3A%22ARIMA%255C+model%22
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Broomandi, Parya (author), Galán-Madruga, David (author), Satyanaga, Alfrendo (author), Hamidi, Mehdi (author), Ledari, Dorna Gholamzade (author), Fathian, Aram (author), Sarvestan, Rasoul (author), Janatian Ghadikolaei, N. (author), Jahanbakhshi, Ali (author)
The Middle East frontal sand and dust storms (SDS) occur in non-summer seasons, and represent an important phenomenon of this region’s climate. Among the mentioned type, spring SDS are the most common. Trend analysis was used in the current study to investigate the spatial-temporal variability of springtime dust events in the Middle East...
journal article 2024
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Retike, Inga (author), Bikše, Jānis (author), Kalvāns, Andis (author), Dēliņa, Aija (author), Avotniece, Zanita (author), Zaadnoordijk, Willem (author), Jemeljanova, Marta (author), Popovs, Konrāds (author), Babre, Alise (author), Zelenkevičs, Artjoms (author), Baikovs, Artūrs (author)
Groundwater level time series are of great value for a variety of groundwater studies, particularly for those dealing with the impacts of anthropogenic and climate change. Quality control of groundwater level observations is an essential step prior to any further application, e.g., trend analysis. Often the quality control of data is limited to...
journal article 2022
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Mu, Li (author), Zheng, Feifei (author), Tao, Ruoling (author), Zhang, Qingzhou (author), Kapelan, Z. (author)
This case study uses a long short-term memory (LSTM)-based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data...
journal article 2020