The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near ground. Here we take an alternative data-driven approach based on supervised learning. We analyze massive datasets measured from anemometers located at 10 m height in 32 locations central north-west Italy. train learning...

Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms prediction, modules that integrate temporal and spatial features existing methods are effective short-term but medium-term or long-term errors relatively large. In order reduce predict speed, this paper proposes a method combines attention Spatial–temporal features, refe...

The coal and gas outburst is one of complex geological disasters and its prediction is influenced by a multiple of factors, such as coal gas, ground stress, physical and mechanical properties, and complex non-linear system, which cause the low prediction accuracy. It is a favorable scheme to use the nonlinear BP neural network for the prediction algorithm design. But, the traditional BP neural ...

— In this work, the non-dominated sorting genetic algorithm–II (NSGA-II) is applied to determine the weights of a neural network trained for short-term forecasting of wind speed. More precisely, the neural network is trained to produce the lower and upper bounds of the prediction intervals of wind speed. The objectives driving the search for the optimal values of the neural network weights are ...

in recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. so in this research, adaptive neuro-fuzzy inference system (anfis) is used for performance of river flow forecasting process. in this research, three parameters such as raining, temperature and daily discharge of lighvanchai basin ...