Search Results (21)

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management. Full article

Groundwater depletion poses a critical challenge to global water security, threatening ecosystems, agriculture, and sustainable development. The Mississippi Delta, a region heavily reliant on groundwater for agriculture, has experienced significant groundwater level declines due to intensive irrigation. Traditional in situ monitoring methods, while valuable, lack the spatial coverage necessary to capture regional groundwater dynamics comprehensively. This study addresses these limitations by leveraging downscaled Gravity Recovery and Climate Experiment (GRACE) data to estimate groundwater levels using random forest modeling (RFM). We applied a machine-learning approach, utilizing the “Forest-based and Boosted Classification and Regression” tool in ArcGIS Pro, (ESRI, Redlands, CA) to predict groundwater levels for April and October over a 10-year period. The model was trained and validated with well-water level records from over 400 monitoring wells, incorporating input variables such as NDVI, temperature, precipitation, and NLDAS data. Cross-validation results demonstrate the model’s high accuracy, with R² values confirming its robustness and reliability. The outputs reveal significant groundwater depletion in the central Mississippi Delta, with the lowest water level observed in the eastern Sunflower and western Leflore Counties. Notably, April 2014 recorded a minimum water level of 18.6 m, while October 2018 showed the lowest post-irrigation water level at 54.9 m. By integrating satellite data with machine learning, this research provides a framework for addressing regional water management challenges and advancing sustainable practices in water-stressed agricultural regions. Full article

The monolithic composite action of structures relies on the interface bond strength between concrete and the repair material. This study uses explainable deep learning techniques to evaluate the ultimate strength capacity (Us) of U-shaped normal concrete (NC) strengthened with polyurethane grouting (PUG) materials. Machine learning algorithms (ML) such as Long Short-Term Memory (LSTM), Random Forest (RF), and Wide Neural Network (WNN) models were developed to estimate Us by considering five input parameters: the initial crack strength (Cs), thickness of the grouting materials (T), mid-span deflection (λ), and peak applied load (P). The results indicated that LSTM models, particularly LSTM-M2 and LSTM-M3, demonstrated superior predictive accuracy and consistency in both the calibration and verification phases, as evidenced by high Pearson’s correlation coefficients (PCC = 0.9156 for LSTM-M2) and Willmott indices (WI = 0.7713 for LSTM-M2), and low error metrics (MSE = 0.0017, RMSE = 0.0418). The SHAP (SHapley Additive exPlanations) analysis showed that the thickness of the grouting materials and maximum load were the most significant parameters affecting the ultimate capacity of the composite U-shaped specimen. The RF model showed moderate improvements, with RF-M3 performing better than RF-M1 and RF-M2. The WNN models displayed varied performance, with WNN-M2 performing poorly due to significant scatter and deviation. The findings highlight the potential of LSTM models for the accurate and reliable prediction of the ultimate strength of composite U-shaped specimens. Full article

Reduced fetal movement (RFM) can indicate that a fetus is at risk, but current monitoring methods provide only a “snapshot in time” of fetal health and require trained clinicians in clinical settings. To improve antenatal care, there is a need for continuous, objective fetal movement monitoring systems. Wearable sensors, like inertial measurement units (IMUs), offer a promising data-driven solution, but distinguishing fetal movements from maternal movements remains challenging. The potential benefits of using linear acceleration and angular rate data for fetal movement detection have not been fully explored. In this study, machine learning models were developed using linear acceleration and angular rate data from twenty-three participants who wore four abdominal IMUs and one chest reference while indicating perceived fetal movements with a handheld button. Random forest (RF), bi-directional long short-term memory (BiLSTM), and convolutional neural network (CNN) models were trained using hand-engineered features, time series data, and time–frequency spectrograms, respectively. The results showed that combining accelerometer and gyroscope data improved detection performance across all models compared to either one alone. CNN consistently outperformed other models but required larger datasets. RF and BiLSTM, while more sensitive to signal noise, offered reasonable performance with smaller datasets and greater interpretability. Full article

The lint harvest index (HI) of cotton is the ratio of cotton lint yield to the total aboveground biomass of cotton, which is not yet clear in arid-zone cotton areas. In 2022–2023, large-scale sampling was carried out in Xinjiang, and the HI of different variety types of cotton in Xinjiang and their key drivers were clarified using methods such as random forest modeling (RFM) and structural equation modeling (SEM). The results show that the overall cotton HI in Xinjiang ranged from 0.276 to 0.333 and 0.279 to 0.328 for the Xinluzao (XLzao) variety types, and from 0.276 to 0.333 for the Xinluzhong (XLzhong) variety types. The results of the SEM analysis show that the latitude (−0.99) and planting density (0.50), in the climatic geography factors, and available potassium in soil (0.88), in the soil nutrient factors, have the greatest effects on the overall cotton HI in Xinjiang. The key driving factors of cotton HI were found to be different among different variety types. This study aimed to clarify the HI of different variety types of cotton in arid-zone cotton and to explore its key driving factors. This was undertaken in order to provide a theoretical basis for the accurate estimation of cotton and cotton straw yields in the arid zone. Full article

This study investigates the latency of lightning-caused fires in the boreal coniferous forests of the Greater Khingan Mountains, employing advanced machine learning techniques to analyze the relationship between meteorological factors, lightning characteristics, and fire ignition and smoldering processes. Using the Random Forest Model (RFM) combined with Recursive Feature Elimination with Cross-Validation (RFECV) and SHapley Additive exPlanations (SHAP), the study identifies key factors influencing fire latency. Two methods, Min distance and Min latency, were used to determine ignition lightning, with the Min distance method proving more reliable. The results show that lightning-caused fires cluster spatially and peak temporally between May and July, aligning with lightning activity. The Fine Fuel Moisture Code (FFMC) and precipitation were identified as the most influential factors. This study underscores the importance of fuel moisture and weather conditions in determining latency of lightning-caused fire, offering valuable insights for enhancing early warning systems. Despite limitations in data resolution and the exclusion of topographic factors, this study advances our understanding of lightning-fire latency mechanisms and provides a foundation for more effective wildfire management strategies under climate change. Full article

This study investigates the application of machine learning techniques for predicting volcanic vent locations based on aeromagnetic geophysical data. Magnetic data, known to reflect subsurface geological structures, presents a valuable source of information for understanding volcanic activity. Leveraging this data, we aim to develop and validate predictive models capable of discerning the presence of volcanic vents. Through a comprehensive data analysis, feature engineering, and model training, we explore the intricate relationships between magnetic variations and volcanic vent locations. Various machine learning algorithms were evaluated for their efficacy in binary classification, with a focus on identifying areas with a high likelihood of volcanic vent presence. The Random Forest model (RFM) was adopted given its high performance metrics, achieving a prediction accuracy of 92%. Our results demonstrate the successful prediction of volcanic vent locations, with a significant correlation of 86% between the actual and predicted vent locations and a high Degree of Certainty (DC) at 97%. This research contributes to the advancement of geospatial data analysis within the field of geoscience, showcasing the potential of machine learning in interpreting and utilizing magnetic data for volcanic hazard assessment and early warning systems. The findings represent a significant step towards enhancing our understanding of volcanic dynamics and improving the predictive tools available for volcanic hazard assessment. Full article

In grassland ecosystems, aboveground biomass (AGB) is critical for energy flow, biodiversity maintenance, carbon storage, climate regulation, and livestock husbandry. Particularly on the climate-sensitive Tibetan Plateau, accurate AGB monitoring is crucial for assessing large-scale grassland livestock capacity. Previous studies focused on predicting AGB mainly at the plant community level and from the perspective of dry weight (AGB_d). This study aims to predict grassland AGB in Xizang at both the plant taxonomic group (sedge, graminoid, forb) and community levels, from both an AGB_d and a fresh weight (AGB_f) perspective. Three to four independent variables (growing mean temperature, total precipitation, total radiation and NDVI_max, maximum normalized difference vegetation index) were used for AGB prediction using nine models in Xizang grasslands. The random forest model (RFM) showed the greatest potential in simulating AGB (training R² ≥ 0.62, validation R² ≥ 0.87). This could be due to the nonlinear relationships between AGB, meteorological factors, and NDVI_max. The RFM exhibited robustness against outliers and zero values resulting from taxonomic groups that were absent from the quadrats. The accuracies of the RFM were different between fresh and dry weight, and among the three taxonomic groups. The RFM’s use of fewer variables can reduce complexity and costs compared to previous studies. Therefore, the RFM emerged as the optimal model among the nine models, offering potential for large-scale investigations into grassland AGB, especially for analyzing spatiotemporal patterns of plant taxonomic groups. Full article

The urban agglomeration on the north slope of the Tianshan Mountains is a pivotal place in Western China; it is essential for the economic growth of Xinjiang and acts as a critical bridge between China’s interior and the Asia–Europe continent. Due to unique natural conditions, the local population distribution exhibits distinct regional characteristics. This study employs the spatial lag model (SLM) from conventional spatial analysis and the random forest model (RFM) from contemporary machine learning techniques. It integrates traditional geographic data, including land cover data and nighttime light data, with geographical big data, such as POI (points of interest) and OSM (OpenStreetMap), to build a comprehensive indicator database. Subsequently, it simulates the spatial population distribution within the urban agglomeration on the northern slopes of the Tianshan Mountains in 2020. The accuracy of the results is then compared and assessed against the accuracy of other available population raster datasets, and the spatial distribution pattern in 2020 is analyzed. The findings reveal the following: (1) The result of SLM, combined with multi-source data, predicts the population distribution as a relatively uniform and nearly circular structure, with minimal spatial differentiation. (2) The result of RFM, employing multi-source data, better captures the spatial population distribution, resulting in irregular boundaries that are indicative of strong spatial heterogeneity. (3) Both models demonstrate superior accuracy in simulating population distribution. The spatial lag model’s accuracy surpasses that of the GHS and GPW datasets, albeit still trailing behind WorldPop and LandScan. Meanwhile, the random forest model significantly outperforms the four aforementioned population raster datasets. (4) The population spatial pattern in the urban agglomeration on the north slope of the Tianshan Mountains predominantly consists of four distinct circles, illustrating a “one axis, one center, and multiple focal points” distribution characteristic. Combining the random forest model with geographic big data for spatialized population simulation offers robust scientific validity and practicality. It holds potential for broader application within the urban agglomeration on the Tianshan Mountains and across Xinjiang. This study can offer insights for studies on regional population spatial distributions and inform sustainable development strategies for cities and their populations. Full article

Accelerometers are powerful tools for behavioral ecologists studying wild animals, particularly species that are difficult to observe due to their cryptic nature or dense or difficult to access habitats. Using a supervised approach, e.g., by observing in detail with a detailed ethogram the behavior of an individual wearing an accelerometer, to train a machine learning algorithm and the accelerometer data of one individual from a wild population of Javan slow lorises (Nycticebus javanicus), we applied a Random Forest model (RFM) to classify specific behaviors and posture or movement modifiers automatically. We predicted RFM would identify simple behaviors such as resting with the greatest accuracy while more complex behaviors such as feeding and locomotion would be identified with lower accuracy. Indeed, resting behaviors were identified with a mean accuracy of 99.16% while feeding behaviors were identified with a mean accuracy of 94.88% and locomotor behaviors with 85.54%. The model identified a total of 21 distinct combinations of six behaviors and 18 postural or movement modifiers in this dataset showing that RFMs are effective as a supervised approach to classifying accelerometer data. The methods used in this study can serve as guidelines for future research for slow lorises and other ecologically similar wild mammals. These results are encouraging and have important implications for understanding wildlife responses and resistance to global climate change, anthropogenic environmental modification and destruction, and other pressures. Full article

The study of urban functional area identification is of great significance for urban function cognition, spatial planning, and economic development. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Aiming at the above problems, this paper proposes a multi-scale recursive recognition method based on interactive validation for urban functional areas using taxi trajectory data and point of interest (POI) data as the main data sources. First, the dynamic time warping (DTW) algorithm generates a time series similarity matrix, a CA-RFM model combining the clustering algorithm and random forest model is constructed. The model extracts significant feature regions as inputs through a K-medoid clustering algorithm, which are imported into the random forest model for urban functional zone (UFZ) identification. Then, to overcome the shortcomings of a single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area, the results show that the combination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the study demonstrates the superiority of the proposed method in two aspects, namely, faster delineation and improved accuracy in urban functional area identification. Full article

The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R² for each model was 0.88. Large R² and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta. Full article

Monitoring livestock allows insights to graziers on valuable information such as spatial distribution, foraging patterns, and animal behavior, which can significantly improve the management of livestock for optimal production. This study aimed to understand what potential variables are significant for predicting where sheep spent the most time in native (NP) and improved (IP) paddocks. Wethers (castrated male sheep) were tracked using Global Positioning System (GPS) collars on 15 sheep in the IP and 15 in the NP, respectively, on a property located in the Monaro region of Southern New South Wales, Australia. Trials were performed over four six-day periods in April, July, and November of 2014 and March in 2015. Data were analyzed to understand various trends that may have occurred during different seasons, using random forest models (RFMs). Of the factors investigated, Normalized Difference Vegetation Index (NDVI) was significant (p < 0.01) and highly important for wethers in the IP, but not the NP, suggesting that quality of pasture was key for wethers in the IP. Elevation, temperature, and near distance to trees were important and significant for predicting residency of wethers in the IP, as well as the NP. The result of this study highlights the ability of predictive models to provide insights on behavior-based modelling of GPS data and further enhance current knowledge about location-based choices of sheep on paddocks. Full article

Lung cancer remains the leading cause for cancer mortality worldwide. While it is well-known that smoking is an avoidable high-risk factor for lung cancer, it is necessary to identify the extent to which other modified risk factors might further affect the cell’s genetic predisposition for lung cancer susceptibility, and the spreading of carcinogens in various geographical zones. This study aims to examine the association between lung cancer mortality (LCM) and major risk factors. We used Fuzzy Inference Modeling (FIM) and Random Forest Modeling (RFM) approaches to analyze LCM and its possible links to 30 risk factors in 100 countries over the period from 2006 to 2016. Analysis results suggest that in addition to smoking, low physical activity, child wasting, low birth weight due to short gestation, iron deficiency, diet low in nuts and seeds, vitamin A deficiency, low bone mineral density, air pollution, and a diet high in sodium are potential risk factors associated with LCM. This study demonstrates the usefulness of two approaches for multi-factor analysis of determining risk factors associated with cancer mortality. Full article

Understanding the spatiotemporal distributions and variation characteristics of water quality parameters is crucial for ecosystem restoration and management of lakes, in particular, Lake Qilu (QL), a typical plateau shallow lake on the Yunnan-Guizhou Plateau, southwestern China. To identify the main causes of harmful algal blooming and continuous water quality decline, the total phosphorus (TP), total nitrogen (TN), water temperature (WT), dissolved oxygen (DO), chlorophyll-a (Chl-a), pH, and turbidity in hypereutrophic Lake Qilu from January 2017 to December 2021 were analyzed. The results showed a complex pattern in spatiotemporal distribution and variation. WT showed no significant change in the vertical profile. DO and pH value variations were caused by both physical and biochemical processes, especially at the bottom of Lake QL with an anaerobic environment. The Trophic State Index (TSI) assessment results showed that Lake QL is a eutrophic (70.14% of all samples, 50 < TSI < 70) to a hypereutrophic lake (29.86%, 70 < TSI) with poor water quality (WQI < 25). TP and WT were the main factors controlling harmful algal blooms (HABs) based on the statistical analysis of Principal Component Analysis (PCA), Random Forest Model (RFM), and Correlation Analysis (CA). In lake QL, TP loading reduction and water level increase might be the key strategies for treating HABs in the future. Based on our results, reducing TP loading may be more effective than reducing TN to prevent HABs in the highly eutrophicated Lake Qilu. Full article