Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods

Khosravi, Vahid; Ardejani, Faramarz Doulati; Yousefi, Saeed; Aryafar, Ahmad

doi:10.1016/j.geoderma.2017.12.025

Cited by 120 publications

(53 citation statements)

References 52 publications

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“…The research results showed that the PSO-BPNN method significantly increased the estimation accuracies of the soil heavy metal contents by greatly decreasing the MRE and RRMSE values. The superiority of PSO-BPNN was attributed to the optimization of the initial input parameters (thresholds and weights) for BPNN by the PSO algorithm, which resulted in the solution for the problem of being stuck in the local minima [8,30]. This implies that PSO-BPNN is very promising in improving the estimation and mapping of the soil heavy metal contents using hyperspectral imagery.…”

Section: Discussionmentioning

confidence: 99%

Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

Liu

et al. 2019

Sustainability

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Soil heavy metals affect human life and the environment, and thus, it is very necessary to monitor their contents. Substantial research has been conducted to estimate and map soil heavy metals in large areas using hyperspectral data and machine learning methods (such as neural network), however, lower estimation accuracy is often obtained. In order to improve the estimation accuracy, in this study, a back propagation neural network (BPNN) was combined with the particle swarm optimization (PSO), which led to an integrated PSO-BPNN method used to estimate the contents of soil heavy metals: Cd, Hg, and As. This study was conducted in Guangdong, China, based on the soil heavy metal contents and hyperspectral data collected from 90 soil samples. The prediction accuracies from BPNN and PSO-BPNN were compared using field observations. The results showed that, 1) the sample averages of Cd, Hg, and As were 0.174 mg/kg, 0.132 mg/kg, and 9.761 mg/kg, respectively, with the corresponding maximum values of 0.570 mg/kg, 0.310 mg/kg, and 68.600 mg/kg being higher than the environment baseline values; 2) the transformed and combined spectral variables had higher correlations with the contents of the soil heavy metals than the original spectral data; 3) PSO-BPNN significantly improved the estimation accuracy of the soil heavy metal contents, with the decrease in the mean relative error (MRE) and relative root mean square error (RRMSE) by 68% to 71%, and 64% to 67%, respectively. This indicated that the PSO-BPNN provided great potential to estimate the soil heavy metal contents; and 4) with the PSO-BPNN, the Cd content could also be mapped using HuanJing-1A Hyperspectral Imager (HSI) data with a RRMSE value of 36%, implying that the PSO-BPNN method could be utilized to map the heavy metal content in soil, using both field spectral data and hyperspectral imagery for the large area.

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Section: Discussionmentioning

confidence: 99%

Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

Liu

et al. 2019

Sustainability

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“…ELM is extensively applied in the prediction task due to its fast learning capability, and adequate generalization performance [39,40]. The combination of ELM with other techniques can enhance the generalization ability of ELM [41][42][43]. Some researchers have successfully used nature-inspired algorithms to optimize ELM.…”

Section: Hybridization (Pso-ann Pso-elm)mentioning

confidence: 99%

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

et al. 2019

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Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (Mr) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r2) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted Mr values generally yielded the same distribution and trend as the observed Mr data.

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“…It is a new efficient learning algorithm for single hidden layer feed-forward neural network structure [22]. As is shown in Figure 6, it consists of an input layer, a hidden layer, and an output layer, and the input layer neurons and the hidden layer neurons are all connected, and the hidden layer neurons and the output layer neurons are all connected.…”

Section: Basic Principles Of Extreme Learning Machine (Elm)mentioning

confidence: 99%

Static and Dynamic Networking of Smart Meters Based on the Characteristics of the Electricity Usage Information

Huang

Sun

2018

Energies

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Abstract:The normal communication between smart meter and concentrator is a key factor influencing the normal function of users' power consumption systems. To solve the communication failure of the smart meter caused by the signal conflict as well as the collected consecutive information abnormality from the same smart meter, according to the chain optimization index, the networking method of static and dynamic combination proposed in this paper is first used to picked out the optimal relay for a smart meter belonging to multiple relay communication ranges. Meanwhile, the communication with other secondary relays is closed to avoid signal conflict. Then the paper forms different combinations of collected data and these combinations are trained in the extreme learning machine (ELM) to find the characteristics value of power consumption information. Finally, in MATLAB simulation, if ELM detects the abnormal information, new communication path could be promptly found through dynamic adjustment of chain optimization weighted coefficient and the weighted coefficient of the number of the relayed smart meters. It solves the problem of consecutive information abnormality from the same smart meter and raises the reliability of smart meter's communication, having a significantly meaning to guarantee the normal function of users' power consumption system.

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Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods

Cited by 120 publications

References 52 publications

Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

Static and Dynamic Networking of Smart Meters Based on the Characteristics of the Electricity Usage Information

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