ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

Neural based geomagnetic forecasting literature has heavily relied upon non-sequential algorithms for estimation of model parameters. This paper proposes sequential Bayesian recurrent neural filters for online forecasting of the Dst index. Online updating of the RNN parameters allows for newly arrived observations to be included into themodel. The online RNN filters are compared to two (non-seq...

In recent years, as the electrical energy of distributed storage has gradually increased, randomness load demand increased. This makes it more difficult to rationally dispatch and store loads. How quickly accurately dig out effective information objective laws from massive power data, effectively clarify instability timing changes, then reduce consumption accidents in dispatching is great signi...

The wind turbine has grown out to be one of the most common Renewable Energy Sources (RES) around the world in recent years. This study was intended to position the Wind Turbine (WT) on a wind farm to achieve the highest performance possible in Electric Distribution Network (EDN). In this paper a new optimization algorithm namely Salp Swarm Algorithm (SSA) is applied to solve the problem of opt...

We use the recently proposed Nested Stochastic Simulation Algorithm (Nested SSA) to simulate the cell cycle model for budding yeast. The results show that Nested SSA is able to significantly reduce the computational cost while capturing the essential dynamical features of the system. AMS subject classifications: 65C05, 60G17, 74A25, 92C42

Gillespie’s Stochastic Simulation Algorithm (SSA) is an exact procedure for simulating the evolution of a collection of discrete, interacting entities, such as coalescing aerosol particles or reacting chemical species. The high computational cost of SSA has motivated the development of more efficient variants, such as Tau-Leaping, which sacrifices the exactness of SSA. For models whose interact...

In the past few years, artiicial neural networks (ANNs) have been investigated as a tool for time series analysis and forecasting. The most popular architecture is the multilayer perceptron, a feedforward network often trained by back-propagation. The forecasting performance of ANNs relative to traditional methods is still open to question although many experimenters seem optimistic. One proble...

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind through an improved al-Biruni Earth radius (BER) metaheuristic algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined optimized achieve superior accuracy prediction. proposed denot...

A landslide is a type of natural disaster that has the highest frequency, widest distribution and heaviest losses worldwide; landslides seriously threaten human life property major engineering facilities. Therefore, it important to improve displacement prediction technology avoid mitigate disasters. method based on chaotic Gaussian mutation sparrow search algorithm-optimised BP neural network (...

Echo state neural networks, which are a special case of recurrent neural networks, are studied from the viewpoint of their learning ability, with a goal to achieve their greater prediction ability. A standard training of these neural networks uses pseudoinverse matrix for one-step learning of weights from hidden to output neurons. Such learning was substituted by backpropagation of error learni...