AIMC Topic: Forecasting

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Improved neural network for predicting blood donations based on two emergent factors.

Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine
BACKGROUND: Blood donation forecasting is a critical part of blood supply chain management. However, few studies have focused on modeling blood donation with different emergency factors. The purpose of this study was to investigate the effects of dif...

Optimal control by deep learning techniques and its applications on epidemic models.

Journal of mathematical biology
We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity analysis is applied to train the neural networks embedded in differential equations. This method can not...

Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model.

Water research
Determination of coagulant dosage in water treatment is a time-consuming process involving nonlinear data relationships and numerous factors. This study provides a deep learning approach to determine coagulant dosage and/or the settled water turbidit...

Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.

Journal of medical systems
Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in...

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities.

Environmental monitoring and assessment
In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used t...

State Causality and Adaptive Covariance Decomposition Based Time Series Forecasting.

Sensors (Basel, Switzerland)
Time series forecasting is a very vital research topic. The scale of time series in numerous industries has risen considerably in recent years as a result of the advancement of information technology. However, the existing algorithms pay little atten...

Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition.

Environmental science and pollution research international
As an efficient, economical, and clean energy, natural gas plays an important role in the development of the new energy revolution. Accurate prediction of natural gas consumption and production can adjust energy deployment in advance, which can ensur...

Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model.

International journal of environmental research and public health
At present, urban flood risk analysis and forecasting and early warning mainly use numerical models for simulation and analysis, which are more accurate and can reflect urban flood risk well. However, the calculation speed of numerical models is slow...

Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction.

Environmental science and pollution research international
Precise wind speed prediction is crucial for the management of the wind power generation systems. However, the stochastic nature of the wind speed makes optimal interval prediction very complicated. In this paper, a hybrid approach consisting of impr...