Abstract Hydraulic support is the primary equipment used for surrounding rock control at fully mechanized mining faces. The load, location, and attitude of hydraulic are important sets basis data to predict roof disasters. This paper summarized analyzed status coal mine safety accidents influencing factors work also proposed monitoring characteristic parameters disasters based on posture-load c...

The growth in electricity consumption the world forces countries to have a well-structured planning relation forecasting demand for their most diverse sectors. Several techniques are used predict electrical loads, such as artificial intelligence models, statistical models and hybrid models. This work aims present model based on combination of method, SARIMA, an neural network, GRNN, improve acc...

Smart city infrastructure has a significant impact on improving the quality of humans life. However, substantial increase in urban population from last few years poses challenges related to resource management, safety, and security. To ensure safety security smart environment, this paper presents novel approach by empowering authorities better visualize threats, identifying predicting highly-re...

Offshore wind power is one of the fastest-growing energy sources worldwide, which environmentally friendly and economically competitive. Short-term time series speed forecasts are extremely significant for proper efficient offshore evaluation in turn, benefit farm owner, grid operators as well end customers. In this study, a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model prop...

We thank members of the CO2 and geothermal working groups at KAUST for valuable input discussions, in particular Faculty Martin Mai Thomas Finkbeiner, Dr. Jakub Fedorik, Miliausha Petrova, Eduardo Torres. Sarima Vahrenkamp Michael Oyinloye are thanked acknowledged their contribution to carbon data collation evaluation. The research is funded by baseline support faculty Vahrenkamp, Afifi Hoteit.

Abstract In this article, the performance evaluation of four univariate time-series forecasting techniques, namely Hyndman Khandakar-Seasonal Autoregressive Integrated Moving Average (HK-SARIMA), Non-Stationary Thomas-Fiering (NSTF), Yeo-Johnson Transformed (YJNSTF) and Seasonal Naïve (SN) method, is carried out. The techniques are applied to forecast rainfall time series stations located in Ke...

Abstract Eutrophication episodes are common in freshwater and coastal environments, causing significant damage to drinking water aquaculture. Predictive models efficient approaches for anticipating eutrophication or algal blooms because ecologists environmentalists can estimate pollution levels take appropriate precautionary steps ahead of time. In aquatic ecosystems, chlorophyll- a (Chl- ) be ...