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Estimation of Soil Heavy Metal Content Using Hyperspectral Data

REMOTE SENSING 2019年第12期11卷

作者： Liu, Zhenhua Lu, Ying Peng, Yiping Zhao, Li Wang, Guangxing Hu, Yueming South China Agr Univ Coll Nat Resources & Environm Guangzhou 510642 Guangdong Peoples R China SIUC Dept Geog & Environm Resources Coll Liberal Arts Carbondale IL 62901 USA South China Agr Univ Guangdong Prov Key Lab Land Use & Consolidat Guangzhou 510642 Guangdong Peoples R China South China Agr Univ Guangdong Prov Engn Res Ctr Land Informat Technol Guangzhou 510642 Guangdong Peoples R China South China Agr Univ Key Lab Construct Land Transformat Minist Land & Resources Guangzhou 510642 Guangdong Peoples R China Qinghai Univ Coll Agr & Anim Husb Xining 810016 Qinghai Peoples R China

Quickly and efficiently monitoring soil heavy metal content is crucial for protecting the natural environment and for human health. Estimating heavy metal content in soils using hyperspectral data is a cost-efficient method but challenging due to the effects of complex landscapes and soil properties. One of the challenges is how to make a lab-derived model based on soil samples applicable to mapping the contents of heavy metals in soil using air-borne or space-borne hyperspectral imagery at a regional scale. For this purpose, our study proposed a novel method using hyperspectral data from soil samples and the HuanJing-1A (HJ-1A) HyperSpectral Imager (HSI). In this method, estimation models were first developed using optimal relevant spectral variables from dry soil spectral reflectance (DSSR) data and field observations of soil heavy metal content. The relationship of the ratio of DSSR to moisture soil spectral reflectance (MSSR) with soil moisture content was then derived, which built up the linkage of DSSR with MSSR and provided the potential of applying the models developed in the laboratory to map soil heavy metal content at a regional scale using hyperspectral imagery. The optimal relevant spectral variables were obtained by combining the boruta algorithm with a stepwise regression and variance inflation factor. This method was developed, validated, and applied to estimate the content of heavy metals in soil (As, Cd, and Hg) in Guangdong, China, and the Conghua district of Guangzhou city. The results showed that based on the validation datasets, the content of Cd could be reliably estimated and mapped by the proposed method, with relative root mean square error (RMSE) values of 17.41% for the point measurements of soil samples from Guangdong province and 17.10% for the Conghua district at the regional scale, while the content of heavy metals As and Hg in soil were relatively difficult to predict with the relative RMSE values of 32.27% and 28.72% at the soil sam

关键词： boruta algorithm dry soil spectral reflectance estimation soil sample soil moisture Guangdong

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Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 794卷 148738页

作者： Tiwari, Anuj Arun, G. Vishwakarma, Bramha Dutt Univ Illinois Syst Discovery Partners Inst Chicago IL 60607 USA Northwestern Univ Ctr Water Res Evanston IL USA Natl Inst Hydrol Ctr Flood Management Studies Gauhati Assam India Univ Bristol Sch Geog Sci Bristol Avon England

Due to ongoing climate change, water mass redistribution and related hazards are getting stronger and frequent. Therefore, predicting extreme hydrological events and related hazards is one of the highest priorities in geosciences. Machine Learning (ML) methods have shown promising prospects in this venture. Every ML method requires training where we know both the output (extreme event) and input (relevant physical parameters and variables). This step is critical to the efficacy of the ML method. The usual approach is to include a wide variety of hydro-meteorological observations and physical parameters, but recent advances in ML indicate that the efficacy of ML may not improve by increasing the number of input parameters. In fact, including unimportant parameters decreases the efficacy of ML algorithms. Therefore, it is imperative that the most relevant parameters are identified prior to training. In this study, we demonstrate this concept by predicting avalanche susceptibility in Leh-Manali highway (one of the most severely affected regions in India) with and without Parameter Importance Assessment (PIA). The avalanche locations were randomly divided into two groups: 70% for training and 30% for testing. Then, based on temporal and spatial sensor data, eleven avalanche influencing parameters were considered. The boruta algorithm, an extension of Random Forest (RF) ML method that utilizes the importance measure to rank predictors, was used and it found nine out of eleven parameters to be important. Support Vector Machine (SVM) based ML technique is used for avalanche prediction, and to be comprehensive, four different kernel functions were employed (linear, polynomial, sigmoid, and radial basis function (RBF)). The prediction accuracy for linear, polynomial, sigmoid, and RBF kernels, with all the eleven parameters were found to be 80.4%, 81.7%, 39.2%, and 85.7%, respectively. While, when using selected parameters, the prediction accuracy for linear,

关键词： Parameter Importance Assessment (PIA) Avalanche susceptibility modeling Machine learning (ML) boruta algorithm Support Vector Machine (SVM)

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Feature Selection for Alzheimer's Gene Expression Data Using Modified Binary Particle Swarm Optimization

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IETE JOURNAL OF RESEARCH 2023年第1期69卷 9-20页

作者： Ramaswamy, Ramya Kandhasamy, Premalatha Palaniswamy, Swathypriyadharsini Bannari Amman Inst Technol Dept Comp Sci & Engn Sathyamangalam India

Alzheimer's Disease (AD) is a neurological disorder that destroys memory and other significant mental functions. One of the most accurate methods to identify the disease-causing genes is to monitor gene expression values in various samples. Selecting significant genes for classification is important in gene expression studies. In this study, the experimental data are taken from the gene expression data of human brain in persons with AD and older control subjects GEO GSE5281 data set. In this work, a new two-step gene selection is applied to filter the noisy and redundant genes, based on the statistical method and heuristic optimization approach. T-statistic (T-test), Signal to Noise Ratio (SNR) and F-test, are used in the first step of the gene selection process. The top ten significant genes selected from the statistical methods are applied to Particle Swarm Optimization (PSO) to obtain the optimal number of features of Alzheimer's disease. To avoid the stagnation issue in PSO, a modified PSO approach is proposed which finds a new particle position by utilizing the Genetic algorithm (GA) crossover and mutation operators. The classifiers, Decision tree, Support Vector Machine (SVM), Linear Model, Random Forest and Neural network, are employed in training and testing data to analyse the performance of GA & PSOs. Modified PSO with t-Test in Random forest and Linear model provides 100% accuracy for the test dataset of GSE5281 with optimum number of genes. The significant genes identified through this research are EGR1, CKMT1B, RPL15, PSMB3, GRK4, COX6A1 and PHIP from the GSE5281 dataset.

关键词： Alzheimer's disease boruta algorithm feature selection gene selection genetic algorithm modi?ed particle swarm optimization random forest linear model

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Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy

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ENVIRONMENTAL POLLUTION 2021年 291卷 118128页

作者： Hong, Yongsheng Chen, Yiyun Shen, Ruili Chen, Songchao Xu, Gang Cheng, Hang Guo, Long Wei, Zushuai Yang, Jian Liu, Yaolin Shi, Zhou Mouazen, Abdul M. Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Univ Ghent Dept Environm Coupure Links 653 B-9000 Ghent Belgium Hubei Acad Environm Sci Wuhan 430072 Peoples R China Zhejiang Univ Coll Environm & Resource Sci Inst Agr Remote Sensing & Informat Technol Applic Hangzhou 310058 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Huazhong Agr Univ Coll Resources & Environm Wuhan 430070 Peoples R China Minist Ecol & Environm South China Inst Environm Sci Guangzhou 510530 Peoples R China

Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.

关键词： Urban and suburban soil Cd contamination Visible-to-near-infrared spectroscopy boruta algorithm Synthetic minority over-sampling technique Machine learning

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Variable selection wrapper in presence of correlated input variables for random forest models

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第1期40卷 297-312页

作者： Rotari, Marta Kulahci, Murat Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark Lulea Univ Technol Dept Business Adm Technol & Social Sci Lulea Sweden

In most data analytic applications in manufacturing, understanding the data-driven models plays a crucial role in complementing the engineering knowledge about the production process. Identifying relevant input variables, rather than only predicting the response through some "black-box" model, is of great interest in many applications. There is, therefore, a growing focus on describing the contributions of the input variables to the model in the form of "variable importance", which is readily available in certain machine learning methods such as random forest (RF). Once a ranking based on the importance measure of the variables is established, the question of how many variables are truly relevant in predicting the output variable rises. In this study, we focus on the boruta algorithm, which is a wrapper around the RF model. It is a variable selection tool that assesses the variable importance measure for the RF model. It has been previously shown in the literature that the correlation among the input variables, which is often a common occurrence in high dimensional data, distorts and overestimates the importance of variables. The boruta algorithm is also affected by this resulting in a larger set of input variables deemed important. To overcome this issue, in this study, we propose an extension of the boruta algorithm for the correlated data by exploiting the conditional importance measure. This extension greatly improves the boruta algorithm in the case of high correlation among variables and provides a more precise ranking of the variables that significantly contribute to the response. We believe this approach can be used in many industrial applications by providing more transparency and understanding of the process.

关键词： additive manufacturing boruta algorithm conditional importance random forest variable selection algorithm

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Sewer Condition Prediction and Analysis of Explanatory Factors

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WATER 2018年第9期10卷

作者： Laakso, Tuija Kokkonen, Teemu Mellin, Ilkka Vahala, Riku Aalto Univ Dept Built Environm POB 15200 Aalto 00076 Finland Aalto Univ Dept Math & Syst Anal POB 11100 Aalto 00076 Finland

Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition.

关键词： boruta algorithm logistic regression partial dependence plot random forest sewer condition variable selection

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Assessment of Municipal Waste Forecasting Methods in Poland Considering Socioeconomic Aspects

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ENERGIES 2024年第14期17卷

作者： Necka, Krzysztof Szul, Tomasz Piotrowska-Woroniak, Joanna Pancerz, Krzysztof Agr Univ Krakow Fac Prod & Power Engn Balicka 116 B PL-30149 Krakow Poland Bialystok Tech Univ HVAC Dept Wiejska 45E PL-15351 Bialystok Poland John Paul II Catholic Univ Lublin Inst Philosophy Al Raclawickie 14 PL-20950 Lublin Poland

As a public service, municipal waste management at the local and regional levels should be carried out in an environmentally friendly and economically justified manner. Information on the quantity and composition of generated municipal waste is essential for planning activities related to the implementation and optimization of the process. There is a need for reliable forecasts regarding the amount of waste generated in each area. Due to the variability in the waste accumulation rate, this task is difficult to accomplish, especially at the local level. The literature contains many reports on this issue, but there is a lack of studies indicating the preferred method depending on the independent variables, the complexity of the algorithm, the time of implementation, and the quality of the forecast. The results concerning the quality of forecasting methods are difficult to compare due to the use of different sets of independent variables, forecast horizons, and quality assessment indicators. This paper compares the effectiveness of selected forecasting models in predicting the amount of municipal waste collection generated in Polish municipalities. The authors compared nine methods, including artificial neural networks (ANNs), support regression trees (SRTs), rough set theory (RST), multivariate adaptive regression splines (MARS), and random regression forests (RRFs). The analysis was based on 31 socioeconomic indicators for 2451 municipalities in Poland. The boruta algorithm was used to select significant variables and eliminate those with little impact on forecasting. The quality of the forecasts was evaluated using eight indicators, such as the absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). A comprehensive evaluation of the forecasting models was carried out using the APEKS method. An analysis of the results showed that the best forecasting methods depended on the set of independent variables and the evaluation cr

关键词： waste utilization municipal waste potential mass index of accumulated municipal waste artificial intelligence methods data selection boruta algorithm APEKS method

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Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load

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ENERGY 2023年 282卷

作者： Tong, Jianfeng Liu, Zhenxing Zhang, Yong Zheng, Xiujuan Jin, Junyang Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China Wuhan Lingjiu Microelect CO LTD Wuhan 430079 Peoples R China Yuanshi Auton Co Ltd Nantong 226010 Peoples R China HUST Wuxi Res Inst Wuxi 214174 Peoples R China

Accurate prediction of gas load is critically important for the stable gas usage and accurate dispatch. In the existing literatures, the prediction accuracy is limited due to the fact that the statistical methods only consider the linear relationships between the gas load and the data-driven seq2seq neural network, which suffers from model compression with shape-based loss function. To address the above issues, an improved multi-gate mixture-of-experts framework is put forward. Firstly, the relevant non-temporal features are selected by analyzing the change pattern of gas consumption, and the boruta algorithm is used to screen the irrelevance and redundancy features. Secondly, convolution network, gated recurrent unit networks and auto regression are chosen as expert networks to acquire both short-term and long-term temporal features, which will be fed into gated network and tower network to achieve multi-step prediction. Finally, the Dilate loss function is used to learn the optimal weight of the designed model considering the dynamics of both shape and temporal. Multi-step prediction experiments with the real constructed gas load dataset verify the effectiveness of the proposed approach, and the evaluation metrics from two perspectives of traditional regression and gas load dispatching verify that the improved multi-gate mixture-of-experts outperforms the state-of-the-art methods.

关键词： Improved multi-gate mixture-of-experts Multi-step prediction Deep learning Dilate loss function boruta algorithm

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Development of an assessment framework for the proposed Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI) for regional drought classifications in global warming context

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年 312卷 114951页

作者： Khan, Muhammad Asif Riaz, Saba Jiang, He Qamar, Sadia Ali, Zulfiqar Islamil, Muhammad Nazeer, Amna Faisal, Muhammad Satti, Samina Zhang, Xiang Jiangxi Univ Finance & Econ Sch Stat Nanchang 330013 Peoples R China SZABIST Islamabad Campus Dept Comp Sci Islamabad Pakistan Univ Sargodha Dept Stat Sargodha Pakistan Tsinghua Univ State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Quaid i Azam Univ Islamabad Dept Stat Islamabad Pakistan COMSATS Univ Stat Dept Islamabad Pakistan Univ Bradford Fac Hlth Studies Bradford BD7 1DP England Bradford Teaching Hosp NHS Fdn Trust Bradford Inst Hlth Res Bradford England Univ Wah Dept Stat Wah Pakistan China Univ Geosci Wuhan Natl Engn Res Ctr Geog Informat Syst Sch Geog & Informat Engn Wuhan 430074 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn Earth Syst & Global Change Lab Shenzhen Peoples R China City Univ Sci & Informat Technol Dept Math Peshawar Pakistan

Drought hazard is one of the main consequences of global warming and climate change. Unlike other natural disasters, drought has complex climatic features. Therefore, accurate drought monitoring is a challenging task. This paper proposes a framework for assessing drought classifications at the regional level. The proposed framework provides a new drought monitoring indicator called Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI). MSARSPEI is an amalgam of the Standardized Precipitation Evapotranspiration (SPEI) (Vicente-Serrano et al., 2010) and Regionally Improved Weighted Standardized Drought Index (RIWSDI) (Jiang et al., 2020). In the proposed framework, the boruta algorithm of feature selection is configured to ensemble monthly time series data of evaporation in various meteorological stations located in specific regions. Further, the framework suggests the standardization of the Cumulative Distribution Function (CDF) of K-Component Gaussian (K-CG) mixture distribution function for obtaining MSARSPEI data. The application of the proposed framework is based on seven different regions of Pakistan. For comparative analysis, this paper compared the performance of MSARSPE with SPEI using Pearson correlation. Outcomes associated with this research show that the proposed regional drought index has a strong correlation with the competing indicator in various time scales. In addition, the study assessed the spatial extent of various drought classifications under MSARSPEI. In summation, this research concludes that the choice of the MSARSPEI is rationally valid and more appropriate for the regional assessment of drought under the global warming scenario.

关键词： Drought monitoring Global warming boruta algorithm K-component Gaussian (K-CG) mixture distribution Standardized precipitation evapotranspiration index (SPEI)

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Analysis of gully erosion susceptibility and spatial modelling using a GIS-based approach

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GEODERMA 2022年 420卷

作者： Wei, Yujie Liu, Zheng Zhang, Yong Cui, Tingting Guo, Zhonglu Cai, Chongfa Li, Zhaoxia Huazhong Agr Univ Coll Resources & Environm Wuhan 430070 Peoples R China Changjiang Water Resources Commiss Yangtze River Basin Monitoring Ctr Stn Soil & Wat Wuhan 430010 Peoples R China

Gully erosion, a dominant limiting factor in promoting ecosystem services and functions, is caused by a wide range of geo-environmental factors. However, limited information is available on the gully erosion mechanisms at a regional scale. Herein, susceptibility of a particular type of large-scale gullies across southern China were analyzed based on the 2005 National Survey on Soil and Water Loss. Specifically, frequency ratio model was used to determine the thresholds of 18 geo-environmental factors, including topography (elevation, slope gradient, slope aspect, slope length, hypsometric integral), distance from primary and tertiary rivers, climate (average annual temperature, precipitation, and rainfall erosivity), lithology, soil texture, soil moisture, vegetation index (NDVI, FVC, and EVI), as well as human activity (LULC and population density), followed by evaluating their relative importance by boruta algorithm, and modelling the gully susceptibility modelling by random forest. The gully susceptibility was showed to be determined by comprehensive effect of all the 18 factors, with the top three important factors including average annual precipitation (9.97%), rainfall erosivity (9.60%), and distance from primary rivers (9.43%), whose thresholds were 1701 - 1872 mm, 5967 - 15676 MJ center dot mm center dot(ha center dot h center dot a)-1, and 20000 - 50000 m, respectively. Moreover, the random forest model with its AUC value ranging from 0.96 to 0.99 performed excellently in the spatial modelling of gully susceptibility. Collectively, the very low, low, moderate, and high susceptibility to these gullies occupied 65.30%, 21.36, 10.61% and 2.73% of southern China, in which the moderate and high susceptibility level were mainly clustered in and around Guangdong Province.

关键词： Gully erosion Random Forest boruta algorithm Susceptibility assessment Geo-environmental factor

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