Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle

Nguyen, Thuy-Anh; Ly, Haï‐Bang; Pham, Binh Thai

doi:10.1155/2020/8845768

Cited by 33 publications

(21 citation statements)

References 67 publications

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“…Ina sequential back-propagation network; weight adaptation was contrived to the framework based on a spontaneous deviation of error [21]. BPN algorithms have been applied to many prediction problems and have become a successful tool for engineers [22]. Traditional or sequential BPN algorithmscan improve the convergence rate for better training [23].…”

Section: Methodsmentioning

confidence: 99%

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Singh

Panda

2022

IJECE

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<p>A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the new streamlined form of a back-propagation algorithm for heterogeneous computing and to examine the coalescence of artificial neural network (ANN) with GPU for predicting the groundwater level. twenty years of data set from 2001-2020 has been taken into consideration for three input parameters namely, temperature, rainfall, and water level for predicting the groundwater level using parallelized backpropagation algorithm on compute unified device architecture (CUDA). This employs the back-propagation algorithm to be best suited to reinforce learning and performance by providing more accurate and fast results for water level predictions on GPUs as compared to sequential ones on central processing units (CPUs) alone.</p>

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Section: Methodsmentioning

confidence: 99%

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Singh

Panda

2022

IJECE

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“…ANNs are also referred to as deep learning models, a term which is used to describe it as an approach to artificial intelligence. ANNs have been widely used in speech and image recognition [33,34] but also in various environmental and health studies [11,[35][36][37][38][39].…”

Section: Model Developmentmentioning

confidence: 99%

Applying Remotely Sensed Environmental Information to Model Mosquito Populations

et al. 2021

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Vector borne diseases have been related to various environmental parameters and environmental changes like climate change, which impact their propagation in time and space. Remote sensing data have been used widely for monitoring environmental conditions and changes. We hypothesized that changes in various environmental parameters may be reflected in changes in mosquito population size, thus impacting the temporal and spatial patterns of vector diseases. The aim of this study is to analyze the effect of environmental variables on mosquito populations using the remotely sensed Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from Landsat 8, along with other factors, such as altitude and water covered areas surrounding the examined locations. Therefore, a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was developed and tested for its ability to predict mosquito populations. The model was applied in NE Greece using mosquito population data from 17 locations where mosquito traps were placed from June to October 2019. All performance metrics indicated a high predictive ability of the model. LST was proved to be the factor with the highest relative importance in the prediction of mosquito populations, whereas the developed model can predict mosquito populations 13 days ahead to allow a substantial window for appropriate control measures.

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“…Zhu et al, applied SVR to predict the soil organic matter content and achieved good prediction results [33]. Nguyen et al, used clay content, natural moisture content, liquid limit, plastic limit, specific gravity, and void ratio as input variables to predict soil internal friction angle using the BPNN model [34]. This paper proposes a new method that predicts the shear strength parameters of agricultural soils using a combination of CPT data and soil properties.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Shear Strength Parameters Using Combined Data and Different Machine Learning Models

et al. 2022

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Soil shear strength is an important indicator of soil erosion sensitivity and the tillage performance of the cultivated layer. Measuring soil shear strength at a field scale is difficult, time-consuming, and costly. This study proposes a new method to predict soil shear strength parameters (cohesion and internal friction angle) by combining cone penetration test (CPT) data and soil properties. A portable CPT measuring device with two pressure sensors was designed to collect two CPT data in farmland, namely cone tip resistance, and cone side pressure. Direct shear tests were performed in the laboratory to determine the soil shear strength parameters for 83 CPT data collection points. Two easily available soil properties (water content and bulk density) were determined via the oven-drying method. Using the two CPT data and the two soil properties as predictors, three machine learning (ML) models were built for predicting soil cohesion and the internal friction angle, including backpropagation neural network (BPNN), partial least squares regression (PLSR), and support vector regression (SVR). The prediction performance of each model was evaluated using the coefficient of determination (R2), the root-mean-square error (RMSE), and the relative error (RE). The results suggested that among all the evaluated models, the BPNN model was the most suitable prediction model for soil cohesion, and the SVR model performed best in predicting soil internal friction angle. Thus, our findings provide a foundation for the convenient and low-cost measurement of soil shear strength parameters.

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Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle

Cited by 33 publications

References 67 publications

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Applying Remotely Sensed Environmental Information to Model Mosquito Populations

Prediction of Soil Shear Strength Parameters Using Combined Data and Different Machine Learning Models

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