Prediction of Hydraulic Blockage at Culverts using Lab Scale Simulated Hydraulic Data

Iqbal, Umair; Riaz, Muhammad Zain Bin; Barthélemy, Johan; Perez, Pascal

doi:10.1080/1573062x.2022.2075770

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…Artificial Intelligence (AI), edge-computing, Artificial Intelligence of Things (AIoT), computer vision and the Internet of Things (IoT) are disruptive technologies that have achieved huge success in dealing with complex real-world problems [ 15 , 16 , 17 , 18 , 19 ]. In the context of waste management, various studies have been performed for waste detection and classification [ 5 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]; however, there still exists a gap in the development of a practical solution.…”

Section: Introductionmentioning

confidence: 99%

Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis

Iqbal

Barthélemy

Perez

et al. 2022

Sensors

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The increased global waste generation rates over the last few decades have made the waste management task a significant problem. One of the potential approaches adopted globally is to recycle a significant portion of generated waste. However, the contamination of recyclable waste has been a major problem in this context and causes almost 75% of recyclable waste to be unusable. For sustainable development, efficient management and recycling of waste are of huge importance. To reduce the waste contamination rates, conventionally, a manual bin-tagging approach is adopted; however, this is inefficient and requires huge labor effort. Within household waste contamination, plastic bags have been found to be one of the main contaminants. Towards automating the process of plastic-bag contamination detection, this paper proposes an edge-computing video analytics solution using the latest Artificial Intelligence (AI), Artificial Intelligence of Things (AIoT) and computer vision technologies. The proposed system is based on the idea of capturing video of waste from the truck hopper, processing it using edge-computing hardware to detect plastic-bag contamination and storing the contamination-related information for further analysis. Faster R-CNN and You Only Look Once version 4 (YOLOv4) deep learning model variants are trained using the Remondis Contamination Dataset (RCD) developed from Remondis manual tagging historical records. The overall system was evaluated in terms of software and hardware performance using standard evaluation measures (i.e., training performance, testing performance, Frames Per Second (FPS), system usage, power consumption). From the detailed analysis, YOLOv4 with CSPDarkNet_tiny was identified as a suitable candidate with a Mean Average Precision (mAP) of 63% and FPS of 24.8 with NVIDIA Jetson TX2 hardware. The data collected from the deployment of edge-computing hardware on waste collection trucks was used to retrain the models and improved performance in terms of mAP, False Positives (FPs), False Negatives (FNs) and True Positives (TPs) was achieved for the retrained YOLOv4 with CSPDarkNet_tiny backbone model. A detailed cost analysis of the proposed system is also provided for stakeholders and policy makers.

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Section: Introductionmentioning

confidence: 99%

Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis

Iqbal

Barthélemy

Perez

et al. 2022

Sensors

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“…The AI-based prediction models are established using large dataset and have emerged as revolutionary in dealing with real-world problems, which involve non-linear relationships. 21,22 Apart from predicting the axial load-carrying T A B L E 1 Empirical equations for GFRP-RC columns.…”

Section: Introductionmentioning

confidence: 99%

“…The AI‐based prediction models are established using large dataset and have emerged as revolutionary in dealing with real‐world problems, which involve non‐linear relationships 21,22 . Apart from predicting the axial load‐carrying capacities of different types of FRP‐RC columns, such approaches have also shown promising results in predicting other structural parameters, such as the compressive strength of concrete, 23 shear strength prediction of steel‐RC beams 24 and flexural strength of concrete beams reinforced with FRP bars 14 .…”

Section: Introductionmentioning

confidence: 99%

Neural network‐based models versus empirical models for the prediction of axial load‐carrying capacities of FRP‐reinforced circular concrete columns

Ali,

Ahmad,

Iqbal

et al. 2023

Structural Concrete

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This study presents new neural‐network (NN)‐based models to predict the axial load‐carrying capacities of fiber‐reinforced polymer (FRP) bar reinforced‐concrete (RC) circular columns. A database of FRP‐reinforced concrete (RC) circular columns having outside diameter and height ranged between 160–305 and 640–2500 mm, respectively was established from the literature. The axial load‐carrying capacities of FRP‐RC columns were first predicted using the empirical models developed in the literature and then predicted using deep neural‐network (DNN) and convolutional neural‐network (CNN)‐based models. The developed DNN and CNN models were calibrated using various neurons integrated in the hidden layers for the accurate predictions. Based on the results, the proposed DNN and CNN models accurately predicted the axial load‐carrying capacities of FRP‐RC circular columns with R2 = 0.943 and R2 = 0.936, respectively. Further, a comparative analysis showed that the proposed DNN and CNN models are more accurate than the empirical models with 52% and 42% reduction in mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively involved in the empirical models. Moreover, within NN‐based prediction models, the prediction accuracy of DNN model is comparatively higher than the CNN model due to the integration of neurons in each layer (9‐64‐64‐64‐64‐1) and embedded rectified linear unit (ReLu) activation function. Overall, the proposed DNN and CNN models can be utilized as paramount in the future studies.

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“…A promising tool for better exploring the relationships between the parameters and influencing factors is machine learning (ML), which has been extensively used within the hydrology community to solve water-related problems (e.g., [31][32][33]). Using ML, efforts have been made to estimate streamflow [34][35][36], runoff signatures [37][38][39][40], soil moisture [41,42], evapotransportation [43,44] and many other water-related variables. Generally, these studies selected several predictors and analyzed the relationship among predictors and target variable based on ML.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

Fang

Huang²,

et al. 2022

Remote Sensing

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The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.

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Prediction of Hydraulic Blockage at Culverts using Lab Scale Simulated Hydraulic Data

Cited by 8 publications

References 40 publications

Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis

Edge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis

Neural network‐based models versus empirical models for the prediction of axial load‐carrying capacities of FRP‐reinforced circular concrete columns

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

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