scholarly journals Soft Computing Techniques for Appraisal of Potentially Toxic Elements from Jalandhar (Punjab), India

2021 ◽  
Vol 11 (18) ◽  
pp. 8362
Author(s):  
Vinod Kumar ◽  
Parveen Sihag ◽  
Ali Keshavarzi ◽  
Shevita Pandita ◽  
Andrés Rodríguez-Seijo
Keyword(s):  
Toxic Elements ◽  
Mean Squared Error ◽  
Agricultural Soils ◽  
Potentially Toxic Elements ◽  
Performance Estimation ◽  
Boosted Regression Trees ◽  
Squared Error ◽  
Soft Computing Techniques ◽  
Models Comparison ◽  
Better Than

The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a pivotal role in eliminating the weaknesses in determining PTEs in soils. This paper aims to predict the concentration of Cu, Co and Pb using neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT). Statistical performance estimation factors were rummage-sale to measure the performance of developed models. Comparison of the coefficient of correlation and root mean squared error suggest that MLP-established models perform better than BT-based models for predicting the concentration of Cu and Pb, whereas BT models perform better than MLP established models at predicting the concentration of Co.

Combining two soil property rasters using an adaptive gating approach

Soil Research ◽  
2015 ◽  
Vol 53 (8) ◽  
pp. 907 ◽  
Author(s):  
David Clifford ◽  
Yi Guo
Keyword(s):  
Confidence Intervals ◽  
Soil Property ◽  
Mean Squared Error ◽  
Geographic Space ◽  
Covariate Information ◽  
Squared Error ◽  
Inverse Variance ◽  
Machine Learning Approach ◽  
Further Development ◽  
Better Than

Given the wide variety of ways one can measure and record soil properties, it is not uncommon to have multiple overlapping predictive maps for a particular soil property. One is then faced with the challenge of choosing the best prediction at a particular point, either by selecting one of the maps, or by combining them together in some optimal manner. This question was recently examined in detail when Malone et al. (2014) compared four different methods for combining a digital soil mapping product with a disaggregation product based on legacy data. These authors also examined the issue of how to compute confidence intervals for the resulting map based on confidence intervals associated with the original input products. In this paper, we propose a new method to combine models called adaptive gating, which is inspired by the use of gating functions in mixture of experts, a machine learning approach to forming hierarchical classifiers. We compare it here with two standard approaches – inverse-variance weights and a regression based approach. One of the benefits of the adaptive gating approach is that it allows weights to vary based on covariate information or across geographic space. As such, this presents a method that explicitly takes full advantage of the spatial nature of the maps we are trying to blend. We also suggest a conservative method for combining confidence intervals. We show that the root mean-squared error of predictions from the adaptive gating approach is similar to that of other standard approaches under cross-validation. However under independent validation the adaptive gating approach works better than the alternatives and as such it warrants further study in other areas of application and further development to reduce its computational complexity.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals Target and Equalizer Design for High-Density Bit-Patterned Media Recording

1970 ◽  
Vol 6 (2) ◽  
pp. 128-135
Author(s):  
Santi Koonkarnkhai ◽  
Phongsak Keeratiwintakorn ◽  
Piya Kovintavewat
Keyword(s):  
Partial Response ◽  
Mean Squared Error ◽  
Bit Patterned Media ◽  
Patterned Media ◽  
Media Noise ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Cross Track ◽  
Partial Response Maximum Likelihood ◽  
Better Than

In bit-patterned media recording (BPMR) channels, the inter-track interference (ITI) is extremely severe at ultra high areal densities, which significantly degrades the system performance. The partial-response maximum-likelihood (PRML) technique that uses an one-dimensional (1D) partial response target might not be able to cope with this severe ITI, especially in the presence of media noise and track mis-registration (TMR). This paper describes the target and equalizer design for highdensity BPMR channels. Specifically, we proposes a two-dimensional (2D) cross-track asymmetric target, based on a minimum mean-squared error (MMSE) approach, to combat media noise and TMR. Results indicate that the proposed 2D target performs better than the previously proposed 2D targets, especially when media noise and TMR is severe.

Ratio-Type Estimation Using Scrabled Auxiliary Variables in Stratification Under Simple Random Sampling and Ranked Set Sampling

2022 ◽  
pp. 62-85
Author(s):  
Carlos N. Bouza-Herrera ◽  
Jose M. Sautto ◽  
Khalid Ul Islam Rather
Keyword(s):  
Random Sampling ◽  
Mean Squared Error ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Auxiliary Variables ◽  
Mild Conditions ◽  
Squared Error ◽  
The Mean ◽  
Better Than

This chapter introduced basic elements on stratified simple random sampling (SSRS) on ranked set sampling (RSS). The chapter extends Singh et al. results to sampling a stratified population. The mean squared error (MSE) is derived. SRS is used independently for selecting the samples from the strata. The chapter extends Singh et al. results under the RSS design. They are used for developing the estimation in a stratified population. RSS is used for drawing the samples independently from the strata. The bias and mean squared error (MSE) of the developed estimators are derived. A comparison between the biases and MSEs obtained for the sampling designs SRS and RSS is made. Under mild conditions the comparisons sustained that each RSS model is better than its SRS alternative.

Potentially toxic elements in agricultural soils from the Lombardia region of northern Italy

2018 ◽  
Vol 190 ◽  
pp. 436-452 ◽  
Author(s):  
Gian Maria Beone ◽  
Franca Carini ◽  
Laura Guidotti ◽  
Riccardo Rossi ◽  
Marina Gatti ◽  
...  
Keyword(s):  
Toxic Elements ◽  
Agricultural Soils ◽  
Northern Italy ◽  
Potentially Toxic Elements

Microbial approach for alleviation of potentially toxic elements in agricultural soils

2020 ◽  
pp. 271-303
Author(s):  
Lander de Jesus Alves ◽  
Fábio Carvalho Nunes ◽  
Irailde da Silva Santos ◽  
Denise Morais Loureiro ◽  
Patricia Alves Casaes ◽  
...  
Keyword(s):  
Toxic Elements ◽  
Agricultural Soils ◽  
Potentially Toxic Elements

Mobility of potentially toxic elements in family garden soils of the Riotinto mining area

2021 ◽  
Author(s):  
Antonio Romero-Baena ◽  
Cinta Barba-Brioso ◽  
Alicia Ross ◽  
Isabel González
Keyword(s):  
Organic Matter ◽  
Toxic Elements ◽  
Agricultural Soils ◽  
Clay Mineralogy ◽  
Mining Area ◽  
Potentially Toxic Elements ◽  
Water Soluble ◽  
Trophic Chain ◽  
Mining Areas ◽  
Total Concentrations

<p>Agricultural soils in mining areas usually accumulate potentially toxic elements (PTEs) that can become a health risk to humans by entering the trophic chain. In this study, five small agricultural plots close to Riotinto mines (SW Spain) were studied, with the aims of comparing the concentration of PTEs with respect to the regional (South Portuguese Zone) baseline and conducting availability studies in order to determine the contamination of soils. Chemical composition, total and clay mineralogy, and edaphic parameters were determined in topsoil and subsoil samples to characterize the soils, and single extractions were conducted to assess the mobility. The mineralogy of the soils was composed of quartz and phyllosilicates, with small amounts of feldspars and occasionally containing hematite and calcite. The texture ranged from sandy to silty loam, the pH was slightly acidic, and high contents of organic matter were found. Total concentrations of trace elements correlated with the texture, the content in iron oxy-hydroxides and the pH. The values of As, Pb, Cu, and Zn exceeded the regional baseline even in sites unaffected by mining. The results suggest that a widespread sampling is necessary to determine the local background. The most water-soluble element was As, due to the competition of organic matter for sorption sites. The content of Cu, Cr and Zn extracted with different methods were higher in sandy soils with low iron oxy-hydroxides content. Monoammonium phosphate and EDTA extractions seemed to remove elements from organic matter and iron oxy-hydroxides. The extracted fractions of As and metals reached up to 10-30 wt%.  Despite the high total concentrations of the element in soils, they generally showed low available proportions, especially with water and ammonium acetate extractants. The results suggest that the soils are not necessarily a risk to humans and higher investigation efforts are necessary to assess the availability of PTEs and their transfer to plants.</p>

scholarly journals A Class of Modified Ratio Estimators for Estimation of Population Variance

2015 ◽  
Vol 11 (1) ◽  
pp. 91-114 ◽  
Author(s):  
J. Subramani ◽  
G. Kumarapandiyan
Keyword(s):  
Mean Squared Error ◽  
Natural Populations ◽  
Auxiliary Variable ◽  
Variance Estimator ◽  
Population Variance ◽  
Study Variable ◽  
Variance Estimators ◽  
Squared Error ◽  
Ratio Estimators ◽  
Better Than

Abstract In this paper we have proposed a class of modified ratio type variance estimators for estimation of population variance of the study variable using the known parameters of the auxiliary variable. The bias and mean squared error of the proposed estimators are obtained and also derived the conditions for which the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators. Further we have compared the proposed estimators with that of the traditional ratio type variance estimator and existing modified ratio type variance estimators for certain natural populations.

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