AIMC Topic: Forecasting

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Improved runoff forecasting based on time-varying model averaging method and deep learning.

PloS one
In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constru...

Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks.

Environmental science and pollution research international
Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the c...

How do I/we forecast tomorrow's transfusion? A focus on recipients' profiles.

Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine
Red blood cell (RBC) transfusion is a life-saving medical intervention and has an essential role in the management of surgical patients. However, blood donations and supply levels are decreasing, therefore there is an unmet need for the accurate pred...

A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting.

Environmental science and pollution research international
The randomness and instability of wind power bring challenges to power grid dispatching. Accurate prediction of wind power is significant to ensure the stable development of power grid. In this paper, a new ultra-short-term wind power forecasting mod...

FASTNN: A Deep Learning Approach for Traffic Flow Prediction Considering Spatiotemporal Features.

Sensors (Basel, Switzerland)
Traffic flow forecasting is a critical input to intelligent transportation systems. Accurate traffic flow forecasting can provide an effective reference for implementing traffic management strategies, developing travel route planning, and public tran...

Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM.

Sensors (Basel, Switzerland)
Multistep power consumption forecasting is smart grid electricity management's most decisive problem. Moreover, it is vital to develop operational strategies for electricity management systems in smart cities for commercial and residential users. How...

Ensemble stacking rockburst prediction model based on Yeo-Johnson, K-means SMOTE, and optimal rockburst feature dimension determination.

Scientific reports
Rockburst forecasting plays a crucial role in prevention and control of rockburst disaster. To improve the accuracy of rockburst prediction at the data structure and algorithm levels, the Yeo-Johnson transform, K-means SMOTE oversampling, and optimal...

Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance ...

Implementation of Machine Learning to Predict Cost of Care Associated with Ambulatory Single-Level Lumbar Decompression.

World neurosurgery
BACKGROUND: With the emergence of the concept of value-based care, efficient resource allocation has become an increasingly prominent factor in surgical decision-making. Validated machine learning (ML) models for cost prediction in outpatient spine s...

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy.

PloS one
The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been ma...