scholarly journals Mapping Soil Organic Matter and Analyzing the Prediction Accuracy of Typical Cropland Soil Types on the Northern Songnen Plain

2021 ◽  
Vol 13 (24) ◽  
pp. 5162
Author(s):  
Meiwei Zhang ◽  
Huanjun Liu ◽  
Meinan Zhang ◽  
Haoxuan Yang ◽  
Yuanliang Jin ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Mean Squared Error ◽  
Critical Role ◽  
Absolute Error ◽  
Bare Soil ◽  
Soil Types ◽  
Coefficient Of Determination ◽  
Songnen Plain ◽  
Type Data

Soil organic matter (SOM) plays a critical role in agroecosystems and the terrestrial carbon cycle. Thus, accurately mapping SOM promotes sustainable agriculture and estimations of soil carbon pools. However, few studies have analyzed the changing trends in multi-period SOM prediction accuracies for single cropland soil types and mapped their spatial SOM patterns. Using time series 7 MOD09A1 images during the bare soil period, we combined the pixel dates of training samples and precipitation data to explore the variation in SOM accuracy for two typical cropland soil types. The advantage of using single soil type data versus the total dataset was evaluated, and SOM maps were drawn for the northern Songnen Plain. When almost no precipitation occurred on or near the optimal pixel date, the accuracies increased, and vice versa. SOM models of the two soil types achieved a lower root mean squared error (RMSE = 0.55%, 0.79%) and mean absolute error (MAE = 0.39%, 0.58%) and a higher coefficient of determination (R2 = 0.65, 0.75) than the model using the total dataset and resulted in a mean relative improvement (RI) of 30.21%. The SOM decreased from northeast to southwest. The results provide reference data for the accurate management of cultivated soil and determining carbon sequestration.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

scholarly journals Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1072 ◽  
Author(s):  
Dong Van Dao ◽  
Hai-Bang Ly ◽  
Huong-Lan Thi Vu ◽  
Tien-Thinh Le ◽  
Binh Thai Pham
Keyword(s):  
Compressive Strength ◽  
Civil Engineering ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Dry Density ◽  
Statistical Measures ◽  
Foamed Concrete

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

scholarly journals ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT

2021 ◽  
Vol 36 (2spl) ◽  
pp. 708-714
Author(s):  
Sayed Mohibul HOSSEN ◽  
◽  
Mohd Tahir ISMAIL ◽  
Mosab I. TABASH ◽  
Ahmed ABOUSAMAK ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Mean Squared Error ◽  
Moving Average ◽  
Critical Role ◽  
Absolute Error ◽  
Tourism Industry ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Autoregressive Integrated Moving Average ◽  
Private And Public

Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.

scholarly journals Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7058
Author(s):  
Heesang Eom ◽  
Jongryun Roh ◽  
Yuli Sun Hariyani ◽  
Suwhan Baek ◽  
Sukho Lee ◽  
...  
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Pressure Sensors ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Proposed Model ◽  
Point Pressure

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.

scholarly journals Evaluation of Random Forest for short-term daily streamflow forecast in rainfall and snowmelt driven watersheds

2020 ◽  
Author(s):  
Leo T. Pham ◽  
Lifeng Luo ◽  
Andrew O. Finley
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Climatic Conditions ◽  
Coefficient Of Determination ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Streamflow Forecast ◽  
Daily Streamflow ◽  
Rf Performance

Abstract. In the past decades, data-driven Machine Learning (ML) models have emerged as promising tools for short-term streamflow forecasts. Among other qualities, the popularity of ML for such applications is due to the methods' competitive performance compared with alternative approaches, ease of application, and relative lack of strict distributional assumptions. Despite the encouraging results, most applications of ML for streamflow forecast have been limited to watersheds where rainfall is the major source of runoff. In this study, we evaluate the potential of Random Forest (RF), a popular ML method, to make streamflow forecast at 1-day lead time at 86 watersheds in the Pacific Northwest. These watersheds span climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes: rainfall-dominated, transisent, and snowmelt-dominated based on the timing of center of annual flow volume. RF performance is benchmarked against Naive and multiple linear regression (MLR) models, and evaluated using four metrics Coefficient of determination, Root mean squared error, Mean absolute error, and Kling-Gupta efficiency. Model evaluation metrics suggest RF performs better in snowmelt-driven watersheds. Largest improvement in forecasts, compared to benchmark models, are found among rainfall-driven watersheds. We obtain Kling–Gupta Efficiency (KGE) scores in the range of 0.62–0.99. RF performance deteriorates with increase in catchment slope and increase in soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.

scholarly journals Forecasting New Student Candidates Using the Random Forest Method

2020 ◽  
Vol 11 (1) ◽  
pp. 44
Author(s):  
Rahmat Robi Waliyansyah ◽  
Nugroho Dwi Saputro
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
College Education ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Squared Error ◽  
Random Forest Method ◽  
Study Programs ◽  
New Students ◽  
New Student

College education institutions regularly hold new student admissions activities, and the number of new students can increase and can also decrease. University of PGRI Semarang (UPGRIS) on the development of new student admissions for the 2014/2015 academic year up to 2018/2019 with so many admissions selection stages. To meet the minimum comparison requirements between the number of students with the development of human resources, facilities, and infrastructure, it is necessary to predict how much the number of students increases each year. To make a prediction system or forecasting, the number of prospective new students required a good forecasting method and sufficiently precise calculations to predict the number of prospective students who register. In this study, the method to be taken is the Random Forest method. For the evaluation of forecasting models used Random Sampling and Cross-validation. The parameter used is Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results of this study obtained the five highest and lowest study programs in the admission of new students. Therefore, UPGRIS will make a new strategy for the five lowest study programs so that the desired number of new students is achieved

scholarly journals Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

2020 ◽  
Vol 17 (10) ◽  
pp. 3520 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Laith Abualigah ◽  
Mohamed Abd Elaziz
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Inference System ◽  
Infected People ◽  
Marine Predators ◽  
Forecasting Performance ◽  
The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

scholarly journals Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection

2021 ◽  
Vol 5 (10) ◽  
pp. 271
Author(s):  
Priyanka Gupta ◽  
Nakul Gupta ◽  
Kuldeep K. Saxena ◽  
Sudhir Goyal
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Fly Ash ◽  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Curing Temperature ◽  
Coefficient Of Determination ◽  
Split Tensile Strength ◽  
Calcined Clay

Geopolymer is an eco-friendly material used in civil engineering works. For geopolymer concrete (GPC) preparation, waste fly ash (FA) and calcined clay (CC) together were used with percentage variation from 5, 10, and 15. In the mix design for geopolymers, there is no systematic methodology developed. In this study, the random forest regression method was used to forecast compressive strength and split tensile strength. The input content involved were caustic soda with 12 M, 14 M, and 16 M; sodium silicate; coarse aggregate passing 20 mm and 10 mm sieve; crushed stone dust; superplasticizer; curing temperature; curing time; added water; and retention time. The standard age of 28 days was used, and a total of 35 samples with a target-specified compressive strength of 30 MPa were prepared. In all, 20% of total data were trained, and 80% of data testing was performed. Efficacy in terms of mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and MSE (mean squared error) is suggested in the model. The results demonstrated that the RFR model is likely to predict GPC compressive strength (MAE = 1.85 MPa, MSE = 0.05 MPa, RMSE = 2.61 MPa, and R2 = 0.93) and split tensile strength (MAE = 0.20 MPa, MSE = 6.83 MPa, RMSE = 0.24 MPa, and R2 = 0.90) during training.

scholarly journals Soil Restoration Practices Effects on Priming Effect Intensity and Carbon Fluxes

2021 ◽  
Author(s):  
Abdourhimou Amadou Issoufou ◽  
Bachirou Hamadou Younoussa ◽  
Sabiou Mahamane ◽  
Idrissa Soumana ◽  
Garba Maman ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Priming Effect ◽  
Critical Role ◽  
Carbon Fluxes ◽  
Soil Restoration ◽  
Strong Impact ◽  
Priming Effects ◽  
C Cycle ◽  
Restoration Practices

Abstract BackgroundThe decomposition of soil organic matter (SOM) is one of the most important processes influencing the global carbon (C) cycle, the physico-chemical characteristics of soils, the mineralization of nutrients for plant growth and soil food webs. Yet, priming effects are considered to be large enough to influence ecosystem carbon fluxes. Here we have tested the effects of soil restoration practices on priming effect and carbon fluxes.ResultOur results suggest that indirect effects are such as altered stabilization of older C associated with the increased inputs of fresh plant inputs (‘priming’) add uncertainty to the prediction of future soil C responses. Far ahead restoration influence the amount and composition of the decomposer organisms, including soil fauna, as well as the soil microbial community, by inducing up to more CO2 emission with fresh millet straw addition in fresh state than pre-decomposed one. Restoration had a very strong impact (increase by 22.7%) on basal soil organic matter mineralization but not on priming effect. The PE of non-restored site was lower than that of restored site by 14.9–22.7%; the lowest mineralization per unit carbon was recorded in the non-restored. Through the “4 per 1000” initiative, it has been very recently demonstrated that priming effect could have a noticeable impact on soil carbon sequestration. ConclusionWe have shown in our study that the degraded soil played a dominant positive role in the soil organic carbon mineralization. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios.

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