|
|
|
|
|
Zitao Du, Wenbo Yang, Yuna Yin, Xinwei Ma and Jiacheng Gong
When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration...
ver más
|
|
|
|
|
|
|
|
|
|
|
Francisca Lanai Ribeiro Torres, Luana Medeiros Marangon Lima, Michelle Simões Reboita, Anderson Rodrigo de Queiroz and José Wanderley Marangon Lima
Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Rejath Jose, Faiz Syed, Anvin Thomas and Milan Toma
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library?a Python-based machine learning toolkit?to construct and refine predictive models for...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shamotra Oad, Monzur Alam Imteaz and Fatemeh Mekanik
Water resources systems planning, and control are significantly influenced by streamflow forecasting. The streamflow in northern and north-central regions of Victoria (Australia) is influenced by different climate indices, such as El Niño Southern Oscill...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ashish Sedai, Rabin Dhakal, Shishir Gautam, Anibesh Dhamala, Argenis Bilbao, Qin Wang, Adam Wigington and Suhas Pol
The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-te...
ver más
|
|
|
|
|
|
|
|
|
|
|
Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo and Francesco Camastra
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep lea...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yajun Wang, Jianping Zhu and Renke Kang
Seasonal?trend-decomposed transformer has empowered long-term time series forecasting via capturing global temporal dependencies (e.g., period-based dependencies) in disentangled temporal patterns. However, existing methods design various auto-correlatio...
ver más
|
|
|
|
|
|
|
|
|
|
|
Liuyi Chen, Bocheng Han, Xuesong Wang, Jiazhen Zhao, Wenke Yang and Zhengyi Yang
With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Gabriela Emiliana de Melo e Costa, Frederico Carlos M. de Menezes Filho, Fausto A. Canales, Maria Clara Fava, Abderraman R. Amorim Brandão and Rafael Pedrollo de Paes
Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Chul-Gyum Kim, Jeongwoo Lee, Jeong Eun Lee and Hyeonjun Kim
This study examines the long-term climate predictability in the Seomjin River basin using statistical methods, and explores the effects of incorporating the duration of climate indices as predictors. A multiple linear regression model is employed, utiliz...
ver más
|
|
|
|
|
|