70 Results Found

This study evaluates the impact of measurement campaign duration on wind resource characterization using three MCP (Measure–Correlate–Predict) models: Total Least Squares (TLS), Multiple Linear Regression (LR), and Quantile Gradient Boost...

Background: Many studies have reported on the causes of chronic lower back pain (CLBP). The aim of this study is to identify if the hierarchical neural network (HNN) is superior to a conventional statistical model for CLBP prediction. Linear models,...

Statistical climate forecast systems typically do not use preceding global gridded sea surface temperature (SST) data directly; instead, they extract a single predictor (e.g., the Niño3.4 index) or multiple predictors (e.g., time series of sev...

Ensuring safe and clean drinking water for communities is crucial, and necessitates effective tools to monitor and predict water quality due to challenges from population growth, industrial activities, and environmental pollution. This paper evaluate...

One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral...

Net ecosystem exchange (NEE) of carbon dioxide (CO₂) was measured in a cool temperate peatland in northwestern Turkey on a continuous basis using eddy covariance (EC) sensors and multiple (non-)linear regression-M(N)LR-models. Our results showed that...

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced...

One of the solutions to deal with water crisis problems is using agricultural residue capabilities as low-cost and the most abundant adsorbents for the elimination of pollutants from aqueous media. This research assessed the potential of activated ca...

By constructing a correlation network between global sea surface temperature anomalies (SSTAs) and summer precipitation anomalies in the Yunnan–Guizhou Plateau, key SST regions influencing summer precipitation anomalies in the Yunnan–Guiz...

Landslide susceptibility mapping is crucial for landslide risk management in mountainous areas like Nepal. However, the performance of a landslide susceptibility model is often compromised by multicollinearity among landslide causative factors. While...

As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts wit...

Research Highlights: this study developed additive biomass equations respectively from nonlinear regression (NLR) on original data and linear regression (LR) on a log-transformed scale by nonlinear seemingly unrelated regression (NSUR). To choose app...

Moderate-to-vigorous physical activity (PA) is recommended to mitigate the risk of diabetes. This study explored the PA of adults at risk for diabetes in rural Indonesia and determined the requirements for meeting the recommended PA level. In total,...

Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn preci...

The ability to predict consumption is an essential tool for the management of a power distribution network. The availability of an advanced metering infrastructure through smart meters makes it possible to produce consumption forecasts down to the le...

Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype c...

Satellite precipitation products play an essential role in providing effective global or regional precipitation. However, there are still many uncertainties in the performance of satellite precipitation products, especially in extreme precipitation a...

Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest neighbors (kNN), random forests (RF), support vector machines (SVM), and boosted decision trees (DTs), may offer stronger predictive performance than more traditional,...

Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled res...

Accurate prediction of forage digestibility is essential for efficient livestock management and feed formulation. This study evaluated the performance of machine learning (ML) models to estimate the in vitro digestibility of leaf and stem components...

In the current shipping industry, quantitative measures of ship fuel consumption (SFC) have become one of the most important research topics in environmental protection and energy management related to shipping operations. In particular, the rapid de...

Portable X-ray fluorescence (pXRF) spectrometers provide simple, rapid, nondestructive, and cost-effective analysis of the metal contents in soils. The current method for improving pXRF measurement accuracy is soil sample preparation, which inevitabl...

Monitoring calf body weight (BW) before weaning is essential for assessing growth, feed efficiency, health, and weaning readiness. However, labor, time, and facility constraints limit BW collection. Additionally, Holstein calf coat patterns complicat...

Accurate and reliable credit risk classification is fundamental to the stability of financial systems and the efficient allocation of capital. However, with the rapid expansion of customer information in both volume and complexity, traditional rule-b...

Text classification is an essential aspect in many applications, such as spam detection and sentiment analysis. With the growing number of textual documents and datasets generated through social media and news articles, an increasing number of machin...

In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth ret...

In the presented study a non-targeted approach using high-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (HPLC-ESI-qToF-MS) combined with chemometric techniques was used to build a sta...

Several studies have attempted to uncover the impact of weather parameters on the coronavirus (COVID-19) pandemic during the initial stage of its outbreak. However, they reported contradicting findings due to limited data available at an earlier stag...

Different methods have been proposed for in situ root-length density (RLD) measurement. One widely employed is the time-consuming sampling of soil cores or monoliths (MO). The profile wall (PW) method is a less precise, but faster and less laborious...

The glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to...

The adjusted binary classification (ABC) approach was proposed to assure that the binary classification model reaches a particular accuracy level. The present study evaluated the ABC for osteometric sex classification using multiple machine learning...

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but de...

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-bas...

Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containi...

Patterns of spatial heterogeneity in forests and other fire-prone ecosystems are increasingly recognized as critical for predicting fire behavior and subsequent fire effects. Given the difficulty in sampling continuous spatial patterns across scales,...

Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destru...

In recent years, new fire loads dominated by power banks have caused multiple fire incidents in transportation hubs, posing severe challenges to fire safety. This study uses experimental testing and machine learning regression analysis to explore the...

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using...

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions...

The influence of climatic variables and land use on fecal coliform (FC) levels in stormwater collected from outfalls throughout southern Vancouver Island between 1995 and 2011 are examined through statistical analyses, Fourier analysis, Multiple Line...

Wildfires are increasingly frequent and severe, posing substantial risks to ecosystems, communities, and infrastructure. Accurately mapping wildfire severity (WSM) using remote sensing and machine learning (ML) is critical for evaluating damages, inf...

The results of metrological, hydrological, and environmental data analyses are mainly dependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing prec...

Soil organic matter (SOM) is an important index to evaluate soil fertility and soil quality, while playing an important role in the terrestrial carbon cycle. The technology of hyperspectral remote sensing is an important method to estimate SOM conten...

Wave-powered desalination systems integrate reverse osmosis (RO) with renewable ocean energy, providing a sustainable and environmentally responsible approach to freshwater production. This study aims to investigate wave-powered RO desalination using...

Background: Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to ref...

Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on predict...

Droughts are among the most critical natural hazards affecting agricultural productivity, water resources, and food security worldwide, with climate change intensifying their frequency and severity. Accurate monitoring and forecasting of drought even...

Traditional leaf chlorophyll estimation using Soil Plant Analysis Development (SPAD) devices and spectrophotometers is a high-cost mechanism in agriculture. Recently, research on chlorophyll estimation using leaf camera images and machine learning ha...

Background: Surgical site infections (SSIs) lead to higher hospital readmission rates and healthcare costs, representing a significant global healthcare burden. Machine learning (ML) has demonstrated potential in predicting SSIs; however, the challen...

Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined com...