To effectively mitigate the hazards posed by the blast waves of rock mass caving on closed walls during the mining process, a stability prediction method based on a random forest (RF) algorithm is proposed, which is designed to automatically identify...
Soil determines the degree of water infiltration, crop nutrient absorption, and germination, which in turn affects crop yield and quality. For the efficient planting of agricultural products, the accurate identification of soil texture is necessary....
This study proposes an explainability-driven feature selection framework for phishing website detection using a large-scale, heterogeneous dataset collected from four independent sources. The combined dataset contains approximately 500,000 samples, i...
This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried...
Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and...
Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspect...
In order to quickly and accurately predict borehole spontaneous combustion danger and avoid borehole spontaneous combustion fires, a borehole spontaneous combustion prediction model combining the Hunger Games search optimization algorithm (HGS) and R...
Concrete is the most widely used material in construction. It has the characteristics of strong plasticity, good economy, high safety, and good durability. As a kind of structural material, concrete must have sufficient strength to resist various loa...
Golf course maintenance requires the use of several inputs, such as pesticides and fertilizers, that can be harmful to human health or the environment. Understanding the factors associated with pesticide use on golf courses may help golf-course manag...
This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely
Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simulations. In contrast, Machine Learning Algorithms (MLA) are (1) fre...
Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral featur...
The steady airflow field on a ship is affected by structure and motion and challenged by phenomena such as the low measurement accuracy of the wind field caused by the occlusion of the anemometer. In this work, an improvement in the accuracy of wind...
The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasti...
The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sen...
Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approac...
Land use change (LUC) driven by human activities and natural factors has resulted in the global loss of native biodiversity and the alteration of ecological processes and services across different ecosystems. It is thus necessary to analyze the trend...
Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we d...
Reclaimed water irrigation is increasingly being applied to address global water scarcity, yet its long-term effects on soil nitrogen cycling and salinity dynamics, particularly in agricultural and agroforestry systems, remain complex and insufficien...
Blast-induced flyrock is one of the most critical hazards in surface mining and quarrying, posing significant risks to occupational safety, nearby structures, and the environment. Accurate prediction of flyrock distance is therefore essential for saf...
Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice....
Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties...
In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process...
Tree species are important factors affecting the carbon sequestration capacity of forests and maintaining the stability of ecosystems, but trees are widely distributed spatially and located in complex environments, and there is a lack of large-scale...
In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies...
Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecologica...
We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influence...
In view of the diversity and varying complexity of ship equipment, and the difficulty of existing state assessment methods in effectively handling the differences in the influence of different characteristic parameters on the equipment’s health...
Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values...
Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classi...
Serious farmland abandonment in hilly areas, and the resolution of commonly used satellite-borne remote sensing images are insufficient to meet the needs of identifying abandoned farmland in such regions. Furthermore, addressing the problem of identi...
Random forests (RF) is a supervised machine learning algorithm, which has recently started to gain prominence in water resources applications. However, existing applications are generally restricted to the implementation of Breiman’s original a...
Permanent magnet synchronous motors (PMSMs) have been gradually used as the driving equipment of coal mine belt conveyors. To ensure safety and stability, it is necessary to carry out real-time and accurate fault diagnosis of PMSM. Therefore, a fault...
The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conserva...
In recent years, with the development of societies and economies, the demand for social electricity has further increased. The efficiency and accuracy of electric-load forecasting is an important guarantee for the safety and reliability of power syst...
Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for amelioratin...
One of the most important hyper-parameters in the Random Forest (RF) algorithm is the feature set size used to search for the best partitioning rule at each node of trees. Most existing research on feature set size has been done primarily with a focu...
The main objectives of this paper are to demonstrate the results of an ensemble learning method based on prediction results of support vector machine and random forest methods using Bayesian average. In this study, we generated susceptibility maps of...
Forest growing stock volume is a crucial indicator for assessing forest resources. However, contemporary machine learning models used in estimating forest growing stock volume often exhibit fluctuating precision and are confined to specific tree spec...
Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily ac...
Spectral, spatial, and temporal features play important roles in land cover classification. However, limitations still exist in the integrated application of spectral-spatial-temporal (SST) features for forest type discrimination. This paper proposes...
The online measurement of the aluminum–silicon ratio of red mud in the dissolution stage of the Bayer alumina production process is difficult to achieve. The offline assay method has a high cost and strong time delay. Soft sensors are an effect...
Ensemble classifiers are being widely used for the classification of spectroscopic data. In this regard, the random forest (RF) ensemble has been successfully applied in an array of applications, and has proven to be robust in handling high dimension...
The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature scree...
Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjun...
Studying the spatial variation patterns and influencing factors of soil organic matter (SOM) in hilly and basin areas is of great significance for guiding agricultural production practices. This study takes Lanxi City as an example and comprehensivel...
Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Vi...
Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network...
Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a t...
In order to accurately predict China’s future total energy consumption, this article constructs a random forest (RF)–sparrow search algorithm (SSA)–support vector regression machine (SVR)–kernel density estimation (KDE) model...
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