Prediction Of Depth Research Articles

Depth Prediction and Reconstruction of Reservoir Surface Temperature based on Multi-data Fusion

Depth Prediction and Reconstruction of Reservoir Surface Temperature based on Multi-data Fusion

Performance Comparison of Several Explainable Hybrid Ensemble Models for Predicting Carbonation Depth in Fly Ash Concrete

The carbonation of fly ash concrete critically impacts the lifespan of structures, necessitating precise prediction of carbonation depth for the construction industry. This study establishes an original database comprising 883 cases, which are divided into training and testing sets in a 4:1 ratio. The Sand Cat Swarm Algorithm (SCSO) was developed to optimize the hyperparameters of three ensemble models: Gradient Boosting Decision Trees (GBDT), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CatBoost), resulting in the development of three hybrid ensemble models: SCSO-GBDT, SCSO-LGBM, and SCSO-CatBoost. Five classic models were included in the comparison, and all models used five-fold cross validation. Models’ performance was rigorously evaluated using Correlation coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF), with VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method and Taylor diagrams utilized for optimal model selection. The SCSO-CatBoost model demonstrated superior performance, with R2 = 0.9657, MAE = 2.0062, RMSE = 8.2147, and VAF = 96.6177. Shapley additive explanations (SHAP) analysis of the SCSO-CatBoost model identified time of exposure as the most significant factor influencing carbonation depth, followed by fly ash content and carbon dioxide concentration. To facilitate practical application by non-algorithm engineers, an intelligent program was developed, allowing for straightforward testing with the three hybrid ensemble models. This study presents three precise models for predicting the carbonation depth of fly ash concrete. These models serve as valuable tools for estimating the service life of concrete structures and can be utilized as simulation instruments in durability engineering.

A dimensionless group-incorporating artificial neural network (DI-ANN) model for single-track depth prediction of SS316L for laser-directed energy deposition (L-DED)

A dimensionless group-incorporating artificial neural network (DI-ANN) model for single-track depth prediction of SS316L for laser-directed energy deposition (L-DED)

Improving melt pool depth estimation in laser powder bed fusion with metallic alloys using the thermal dose concept

Laser-matter interactions in laser powder bed fusion for metals (LPBF-Ms) significantly impact the final properties of the fabricated components. Critical process parameters, such as the linear energy density (LED), the ratio of laser power to scan speed, modify the energy input and consequently modify the melt pool geometry. LED strongly influences the melt pool cross-sectional profile, which dictates the thermal effects, microstructure, and mechanical properties of the finished part. Recognizing the crucial role of the melt pool in additive manufacturing, researchers have developed predictive models to estimate its dimensions and morphology. These models aid in tailoring part properties, optimizing process parameters, and reducing the number of experimental trials. However, existing models are either computationally expensive or analytically overly simplified for general LPBF-M applications. This study proposes an improved model that incorporates the Rosenthal equation as described by Tang to increase the accuracy of melt pool depth prediction. By using the thermal gradient per unit time, termed the “thermal dose” in this paper, corresponding to the LED value that produces experimental near-semicircular melt pool shapes for each studied material, we can improve the melt pool depth estimation. The trend revealed a good fit across the LED range compared with experimental measurements, suggesting the model’s effectiveness.

Advanced Prediction Models for Scouring Around Bridge Abutments: A Comparative Study of Empirical and AI Techniques

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

Sensitivity of Dust Deposition for Parabolic Trough Collector Mirrors to Different Meteorological Drivers

This abstract presents a soiling forecasting tool (SFT) for assessing the deposition of dust on parabolic trough collector (PTC) mirrors and its cumulative impact on their reflectivity. The SFT was developed by the University of Patras in the frame of the Smart Solar System (S3) project (Horizon 2020 Solar-Era.net). An initial version of the model was presented at the SolarPACES 2022 conference, where the adaptation of the model to dust transport phenomena was demonstrated. The subsequent step in the evolution of the algorithm concerned the calibration of the SFT under different atmospheric conditions. In addition, the model was modified to incorporate meteorological and aerosol forecast data as inputs. The atmospheric predictions of dust and aerosol optical depth are from the global atmospheric forecasts of the Copernicus Atmosphere Monitoring Service. Regarding the meteorological data, three independent sources are used. Those are the Norwegian Meteorological Institute's meteorological forecasts (YR), the METAR meteorological observations from the closest airport, and lastly, the data from the automatic weather station at the location of the PTC system at the KEAN Soft Drinks Ltd factory in Limassol, Cyprus. The aim of this paper is to assess the impact of the three distinct meteorological input sources on the modelled reflectivity and its comparison to measurements taken during the experimental campaign.

Pixel-wise matching cost function for robust light field depth estimation

Depth estimation has significant potential in industrial applications, such as robot navigation. Light Field (LF) technology captures both spatial and angular information of a scene, enabling precise depth acquisition. Stereo matching technology has been commonly used for this task in LF imaging. However, previous methods typically compute the matching cost by simply shifting images with predefined offsets, causing poor performance in regions with sudden depth distribution changes (e.g., edges). To address this issue, we propose a Pixel-wise Matching Cost Function (PMCF) to estimate depth in units of pixel, then design a pixel-based network, termed PixelNet, for depth prediction, which achieves top rankings on the HCI 4D light field benchmark. Specifically, our cost function consists of two modules: the range search strategy and the modulation mechanism. The range search strategy enables the network to iteratively optimize depth on a per-pixel basis, while the modulation mechanism effectively handles scene noise and occlusions. By integrating these two modules, our function enables the generation of depth maps with sharper edges and smoother surfaces, compared with previous methods. Finally, we design a pixel-based network and apply it to both synthetic and real-world scenes, demonstrating that our method outperforms state-of-the-art methods in both scenes. Furthermore, PixelNet can also function as a post-processing network that can be seamlessly integrated with existing methods for depth refinement.

Concrete carbonation depth prediction model based on a gradient-boosting decision tree and different metaheuristic algorithms

Carbonation is a significant factor contributing to the instability of concrete structures. This study proposes two prediction models based on the gradient boosting decision tree (GBDT) to address the challenge of predicting concrete carbonation depth. This study integrates GBDT with two metaheuristic algorithms, particle swarm optimization algorithm (PSO) and sparrow search optimization algorithm (SSA), forming two hybrid models, PSO-GBDT and SSA-GBDT, aimed at enhancing prediction accuracy. Six influencing parameters (FA, t, w/b, B, RH, and CO2) were selected as input features to train and evaluate the hybrid models, with the concrete carbonation depth was used as the output. A database containing 883 groups of cases was established. Finally, three classic models, GBDT, ANN and SVR, were compared against the two hybrid models. To enhance model generalization, all models were subjected to five-fold cross validation. Four evaluation indicators (RMSE, R2, MAE, and VAF) and Taylor diagrams were employed to comprehensively assess these models. The results indicated that the two hybrid models exhibited superior prediction performance in the dataset (training set: R2, VAF>0.98, testing set: R2, VAF>0.96). The SSA-GBDT model achieved the highest prediction performance (RMSE= 2.7008, R2= 0.9639, MAE= 1.7691, VAF= 0.9627). Additionally, the SSA-GBDT model identified exposure time (t) as the feature with the greatest impact. This study demonstrates the applicability of the SSA-GBDT models in predicting concrete carbonation depth, offering a novel approach for improving accuracy in such predictions.

Real-time, dense UAV mapping by leveraging monocular depth prediction with monocular-inertial SLAM

We present a dense and metric 3D mapping pipeline designed for embedded operation on-board UAVs, by loosely coupling deep neural networks trained to infer dense depth single images with a SLAM system that restores metric scale from sparse depth. In contrast to computationally restrictive approaches that leverage multiple views, we propose a highly efficient, single-view approach without sacrificing 3D mapping performance. This enables real-time construction of a global 3D voxel map by iterative fusion of the rescaled dense depth maps obtained via raycasting from the estimated camera poses. Quantitative and qualitative experimentations of our framework in challenging environmental conditions show comparable or superior performance with respect to state-of-the-art approaches via a better effectiveness-efficiency trade-off.

A probabilistic time-dependent corrosion wastage model for offshore platforms based on reverse diffusion model

Corrosion wastage poses significant challenges to offshore platforms, making accurate corrosion depth prediction crucial for structural integrity and extending service life. This study addresses the limitations in measurement processes by integrating a reverse diffusion model into the traditional probabilistic corrosion wastage model, enabling comprehensive analysis of corrosion data. The reverse diffusion model effectively manages data uncertainty and noise, leading to a proposed probabilistic model that incorporates measurement uncertainty. This model not only mitigates data noise and errors but also improves the reliability and robustness of corrosion depth predictions for offshore platforms. Initially, statistical analysis of corrosion data from offshore mobile platforms confirmed that the corrosion rate and depth distribution of local components follow a Weibull distribution. The effectiveness of the reverse diffusion model was validated by comparing the probability distributions of original data with those processed using the model. Finally, leveraging the optimized data and an empirical corrosion model for ships, a probabilistic model for time-dependent corrosion wastage in offshore platforms was developed, and its reliability was assessed. The proposed model enhances the accuracy of corrosion predictions, contributing to better management of corrosion risks in offshore platform structures.

The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning

AbstractHydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US.

Comparison of ultra-wide band microwave system and ultrasound in live cattle to predict beef carcase subcutaneous fatness

Ultrasound and ultrawide band microwave system (MiS) were directly compared in their ability to scan live cattle to predict carcase traits. Commercial beef cattle (n = 315) were scanned on farm 0–14 days prior to slaughter. Traits measured were subcutaneous fatness at the P8 site (over the gluteus muscle on the rump, at the intersection of a line through the pin bone parallel to the chine and perpendicular through the 3rd sacral crest) and subcutaneous fatness at the rib fat site (between 12th & 13th rib, ¾ of the length ventrally over the longissimus muscle). The precision of prediction of carcase traits was slightly better using MiS. MiS prediction of P8 fat depth had an average RMSEP of 2.48 mm and R2 of 0.65. The MiS could predict carcase rib fat with an average RMSEP of 2.28 mm and R2 of 0.56. The accuracy of prediction was very similar between the two technologies. When predicting P8, the average bias was smallest using MiS at 0.157 mm, but the average slope was smallest using ultrasound at 0.03 mm. When predicting rib fat, MiS had the smallest average bias at 0.204 mm, and smallest average slope deviation at 0.06 mm. The MiS predicted P8 and rib fat carcase traits with similar precision and accuracy as ultrasound.

Physics‐Informed Neural Networks for the Augmented System of Shallow Water Equations With Topography

AbstractPhysics‐informed neural networks (PINNs) are gaining attention as an alternative approach to solve scientific problems governed by differential equations. This work aims at assessing the effectiveness of PINNs to solve a set of partial differential equations for which this method has never been considered, namely the augmented shallow water equations (SWEs) with topography. Differently from traditional SWEs, the bed elevation is considered as an additional conserved variable, and therefore one more equation expressing the fixed‐bed condition is included in the system. This approach allows the PINN model to leverage automatic differentiation to compute the bed slopes by learning the topographical information during training. PINNs are here tested for different one‐dimensional cases with non‐flat topography, and results are compared with analytical solutions. Though some limitations can be highlighted, PINNs show a good accuracy for the depth and velocity predictions even in the presence of non‐horizontal bottom. The solution of the augmented system of SWEs can therefore be regarded as a suitable alternative strategy to deal with flows over complex topography using PINNs, also in view of future extensions to realistic problems.

The Methods for Estimating Lake Volume, Mean Depth, and Maximum Depth in European Standard EN 16039:2011 Are Flawed and Should Not Be Used

ABSTRACTThe European Standard EN 16039:2011 provides guidelines for assessing lake hydromorphology within the Water Framework Directive and includes methods for estimating volume, mean depth, and maximum depth based on statistical models applied to data from topographic maps. We tested the predictive accuracy of these models using independently collected bathymetric data from 35 Swedish lakes. The models had no predictive power, and the maximum depth predictions were inversely correlated with the observed values. The mean absolute percent error was 46% for volume and mean depth, and 85% for maximum depth. The models are flawed and should not be used.

Utilizing incineration bottom ash-infused steel fiber-reinforced concrete: An analysis of compressive strength and carbonation durability

This study has developed concrete with moderate strength, and excellent carbonation resistance, by blending incineration bottom ash with steel fibers into the concrete mixture. Through single-factor experiments, investigate the effects of bottom ash content and steel fiber volume fraction on the mechanical properties and carbonation resistance of concrete. The results indicate that the addition of steel fibers effectively reduces the adverse effects of incineration bottom ash content on the compressive strength and carbonation level of concrete. When the bottom ash content is 20 % and the steel fiber content is 2 %, the compressive strength of concrete reaches 56.1 MPa, and the carbonation depth drops to 4.19 mm after 28 days. In addition, a carbonation depth prediction model was established based on the compressive performance of concrete, accurately predicting the carbonation depth at different carbonation stages.

Analysis of Flood Resistance of Masonry Arch Bridges Combining Numerical Simulation and Probabilistic Analysis

ABSTRACT In the context of global climate change, extreme weather events such as heavy rainfall and floods are becoming more frequent. The safety of masonry arch bridges is facing unprecedented challenges. This study aims to explore the collapse mechanism of bridges in flood environments by combining numerical simulation and stochastic analysis. Taking the collapse of the Original Zhenhai Bridge caused by heavy rainfall in July 2020 as a specific case, a three-dimensional numerical model is established to analyze the mechanical response of the bridge under the combined action of flood scour and water flow loads. Additionally, predictions of scour depth are made using both theoretical formulas and stochastic methods, comparing the impact of stochastic analysis on the mechanical performance of arch bridges under the same flood conditions. The study reveals that compared to pure scour, the structural characteristics of the bridge undergo significant changes under the coupled effects of scour and water flow loads. In extreme flood events, stochastic analysis has a significant impact on the structural safety under high flow conditions. The research results provide case references and scientific methods for the safety analysis of masonry arch bridges in flood environments.

Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7~414.9 m and 750~900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified as deep mining. Based on this boundary, CatBoost prediction models for the depth of coal seam floor failure in deep and shallow parts were constructed and the prediction results of the model test set were compared with the calculation results of the empirical formula. These models exhibited superior accuracy with a lower mean squared error (MSE) and mean absolute error (MAE) and a higher R-squared (R2) compared to the empirical formula. This study helps to enhance the understanding of coal seam floor behavior, guide floor management, and protect groundwater resources by defining deep and shallow mining to accurately predict floor failure depth.

Machine Learning for the Sustainable Management of Depth Prediction and Load Optimization in River Convoys: An Amazon Basin Case Study

The seasonal fluctuation of river depths is a critical factor in designing cargo capacity for river convoys and logistics processes used for grain transportation in northern Brazil. Water level variations directly impact the load capacities of pusher convoys navigating the Amazon rivers. This paper presents a machine learning model based on a multilayer perceptron artificial neural network developed with the aim of estimating the cargo capacities of river convoys one year in advance, which is essential for determining load capacities during dry periods. The prediction model was applied to the Tapajós River in the Amazon Basin, Brazil, where grain transportation is significant and relies on inland waterways. Navigability conditions were evaluated in terms of depth and geometric parameters. The results of this case study were satisfactory, validating the computational tool and enabling the assessment of capacity losses during dry periods and the identification of navigation bottlenecks. The main contributions of this work include optimizing river logistics, reducing costs, minimizing environmental impacts, and promoting the sustainable management of water resources in the Amazon. Conclusions drawn from the study indicate that the developed model is highly effective, with an R2 of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region.

Aerosol optical depth prediction based on dimension reduction methods

Aerosol optical depth prediction based on dimension reduction methods

Comparison of two gamma-ray datasets measured with different methods and assessment of their performance to predict soil properties

ABSTRACT Terrain mobility is directly dependent on soil properties, i.e. stoniness, soil particle size and peat depth. Gamma-ray spectrometry has been widely used in soil mapping as gamma-rays can penetrate up to 50 cm of soil. The method has proven to be effective in gathering information on various soil properties, such as clay and silt content, organic carbon content, soil pH, and soil stoniness. The intensity of gamma radiation decreases with increasing soil moisture content, which is important when assessing the load-bearing capacity of forest terrain, which affects the quality and planning of mechanized wood harvesting. In this study, we compared two gamma-ray datasets that were acquired from the same plots but measured with different methods: airborne gamma-ray spectrometry and a handheld gamma-ray spectrometer. Regression analyses were used to examine whether the gamma-ray datasets have significant similarities. Linear discriminant analysis was then used to study the performance of the two gamma-ray datasets when predicting different soil properties (stoniness index classes (SIC), soil depth, and peat depth). Our correlation analysis produced R2 values of 0.18–0.29 and root mean square error (RMSE%) values of 0.48–0.55. Prediction of SIC was 67.1% accurate with ground gamma and 45.9% accurate with air gamma. For soil depth predictions, the accuracies were 64.7–70.0% for ground gamma and 61.7–63.5% for air gamma. Our findings showed that ground gamma-ray spectrometry is better at predicting soil properties than air gamma-ray spectrometry.