Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH...
Shear wave velocity (VS) is one of the most important parameters in deep and surface studies and the estimation of geotechnical design parameters. This parameter is widely utilized to determine permeability and porosity, lithology, rock mechanical pa...
Proper performance of water distribution networks (WDNs) plays a vital role in customer satisfaction. The aim of this study is to conduct a sensitivity analysis to evaluate the behavior of WDNs analyzed by a pressure-driven analysis (PDA) approach an...
Investigation of Water Distribution Networks (WDNs) is considered a challenging task due to the unpredicted and uncertain conditions in water engineering. When in a WDN, a pipe failure occurs, and shut-off valves to isolate the broken pipe to allow r...
Energy management in power systems is influenced by such factors as economic and ecological aspects. Increasing the use of electricity produced at a given time from renewable energy sources (RES) by employing the elastic energy management algorithm w...
Evaluating road safety is an enduring research topic in Infrastructure and Transportation Engineering. The prediction of crash risk is very important for avoiding other crashes and safeguarding road users. According to this task, awareness of the num...
Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex...
This paper presents data-driven modeling and a results analysis. Group method data handling (GMDH), multivariable regression (MVR), artificial neuron network (ANN), and new proposed GMDH-featured ANN machine learning algorithms were implemented to mo...
As objects of load prediction are becoming increasingly diversified and complicated, it is extremely important to improve the accuracy of load forecasting under complex systems. When using the group method of data handling (GMDH), it is easy for the...
Although the group method of data handling (GMDH) is a self-organizing metaheuristic neural network capable of developing a classification function using influential input variables, the results can be improved by using some pre-processing steps. In...
Total dissolved solid prediction is an important factor which can support the early warning of water pollution, especially in the areas exposed to a mixture of pollutants. In this study, a new fuzzy-based intelligent system was developed, due to the...
The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes hav...
This study presents a novel method for predicting the undrained shear strength (cu) using artificial intelligence technology. The cu value is critical in geotechnical applications and difficult to directly determine without laboratory tests. The grou...
There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D wa...
With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factor...
Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River ba...
Fiber-reinforced polymer (FRP) is widely used in the field of structural engineering, for example, as a confining material for concrete. The ultimate conditions (i.e., compressive strength and ultimate axial strain) are key factors that need to be co...
The bearing temperature forecasting provide can provide early detection of the gearbox operating status of wind turbines. To achieve high precision and reliable performance in bearing temperature forecasting, a novel hybrid model is proposed in the p...
The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effecti...
Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care...
Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologi...
The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic...
The optimum design of tall buildings, which have a proportionately huge quantity of structural elements and a variety of design code constraints, is a very computationally expensive process. In this paper, a novel strategy, with a combination of evol...
The digital economy (DE) is increasingly associated with higher carbon productivity (CP) and is widely regarded as an important factor in efforts to achieve the dual-carbon goals. However, the formulation of differentiated policies is constrained by...
A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mo...
In recent decades, thin-walled composite components have been widely used in the automotive industry due to their high specific energy absorption. A large number of experimental and numerical studies have been conducted to characterize the energy abs...
Harmonic generation in power system networks presents significant issues that arise in power utilities. This paper describes a machine learning technique that was used to conduct a research study on the harmonic analysis of railway power stations. Th...
Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of va...
With the increasing level of intelligence of marine engines, there is an increasing demand for the online monitoring of engines, and marine NOx emissions have been of great concern. In this paper, a NOx simulation model is developed based on virtual...
The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active...
The current study developed storm surge hindcast/forecast models with lead times of 5, 12, and 24 h at the Sakaiminato port, Tottori, Japan, using the group method of data handling (GMDH) algorithm. For training, local meteorological and hydrodynamic...
The air kerma, which is the amount of energy given off by a radioactive substance, is essential for medical specialists who use radiation to diagnose cancer problems. The amount of energy that a photon has when it hits something can be described as t...
Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varyin...
Carbon dioxide-based enhanced oil-recovery (CO2-EOR) processes have gained considerable interest among other EOR methods. In this paper, based on the molecular weight of paraffins (n-alkanes), pressure, and temperature, the magnitude of CO2–n-alkanes...
It is feared that the increasing population of vehicles in the world and the depletion of fossil-based fuel reserves could render transportation and other activities that rely on fossil fuels unsustainable in the long term. Concerns over environmenta...
Asphaltenes deposition is considered a serious production problem. The literature does not include enough comprehensive studies on adsorption phenomenon involved in asphaltenes deposition utilizing inhibitors. In addition, effective protocols on hand...
The ultimate axial bearing capacity (UABC) of a single pile is an important parameter in pile design. BP neural network (BPNN) has a strong nonlinear mapping ability and can effectively predict the UABC of a single pile. However, frequent immersion i...
Given that the primary cause of flooding in Ontario, Canada, is attributed to spring floods, it is crucial to incorporate temperature as an input variable in flood prediction models with machine learning algorithms. This inclusion enables a comprehen...
Determining the volume percentages of flows passing through the oil transmission lines is one of the most essential problems in the oil, gas, and petrochemical industries. This article proposes a detecting system made of a Pyrex-glass pipe between an...
Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling t...
In this study, various machine learning algorithms, including the minimax probability machine regression (MPMR), functional network (FN), convolutional neural network (CNN), recurrent neural network (RNN), and group method of data handling (GMDH) mod...
The quality of coking coal is vital in steelmaking, impacting final product quality and process efficiency. Conventional forecasting methods often rely on empirical models and expert judgment, which may lack accuracy and scalability. Previous researc...