scholarly journals Prediction of soil loss due to erosion using support vector machine model

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Van Quan Tran ◽  
Indra Prakash
Keyword(s):  
Support Vector Machine ◽  
Soil Erosion ◽  
Vegetation Cover ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Mean Squared Error ◽  
Support Vector ◽  
Squared Error ◽  
Model Study ◽  
Loss Prediction

Soil erosion refers to the detachment and removal of soil particles from land (topsoil), by the natural physical forces (water, glacier and wind). Soil erosion causes soil loss in the catchment or any land areas severely impacting agriculture activity, sedimentation in the dam reservoirs, and hampering developmental activities. Therefore, it is desirable to accurately measure soil loss due to erosion for the development and management of an area. With this objective, a well-known machine learning algorithm Support Vector Machine (SVM) has been used in the development of the soil loss prediction model. Eight erosion affecting variable inputs: ambient temperature Tair, rainfall, Antecedent Moisture Conditions (AMC), rainfall intensity, slope, vegetation cover, soil temperature Tsoil and moisture of the soil. Data on published literature was used in the model study. The accuracy of the proposed SVM was assessed by using three statistical performance evaluation indicators namely Person correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Squared Error (MAE). Partial Dependence Plots (PDP) was used to investigate the dependence of prediction results of eight input variables used in the model study. Model validation results showed that SVM model performed well for the prediction of soil loss for testing (R = 0.8993) and also for training (R=0.9123). Rainfall intensity and vegetation cover were found to be the two most important affecting input parameters for the soil loss prediction.

scholarly journals Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory

2021 ◽  
Vol 5 (3) ◽  
pp. 466-473
Author(s):  
Azam Zamhuri Fuadi ◽  
Irsyad Nashirul Haq ◽  
Edi Leksono
Keyword(s):  
Support Vector Machine ◽  
Energy Management ◽  
Mean Squared Error ◽  
Measurement Data ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Error ◽  
Squared Error ◽  
Electrical Loads

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.

Visible-near infrared spectroscopy for detection of blood in sheep faeces

2020 ◽  
Vol 28 (5-6) ◽  
pp. 255-266 ◽  
Author(s):  
Elise A Kho ◽  
Jill N Fernandes ◽  
Andrew C Kotze ◽  
Glen P Fox ◽  
Maggy Lord ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error

Heavy infestations of the blood-sucking gastrointestinal nematodes, Haemonchus contortus can cause severe anaemia in sheep and leakage of blood into the faeces, leading to morbidity and mortality. Early and accurate diagnosis of infections is critical for timely treatment of sheep, minimizing production and sheep welfare impacts. In pursuit of a quick and easy measure of H. contortus infections, we investigated the use of portable visible near infrared spectrometers for detecting the presence of haemoglobin in sheep faeces as an indicator of H. contortus infection. Calibration models built within the 400–600 nm region by partial least square regression resulted in acceptable prediction accuracies (r 2 p > 0.70 and root mean squared error of prediction <2.64 µg Hb mg−1 faeces) for haemoglobin quantification using two spectrometers. The prediction results from support vector machine regression further improved the prediction of haemoglobin in moist sheep faeces (r 2 p > 0.87 and root mean squared error of prediction <2.00 µg haemoglobin mg−1 faeces). Based on a threshold for anthelmintic treatment of 3 µg Hb mg−1 faeces, both the partial least square and support vector machine models showed high sensitivity (89%) and high specificity (>77%). The specificity of the prediction model for detecting haemoglobin in sheep faeces may be improved by adding more variations in faecal composition into the calibration model. Our success in detecting haemoglobin in sheep faeces, following minimal sample preparation, suggests that with further development, vis–near infrared spectroscopy can provide a sensitive and convenient method for on-farm diagnosis of H. contortus infections.

scholarly journals Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine

2019 ◽  
Vol 9 (9) ◽  
pp. 1761 ◽  
Author(s):  
Zhifang Wang ◽  
Shutao Wang ◽  
Deming Kong ◽  
Shiyu Liu
Keyword(s):  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Methane Concentration ◽  
Mean Squared Error ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Squared Error ◽  
Svm Model ◽  
Methane Detection

Methane, known as a flammable and explosion hazard gas, is the main component of marsh gas, firedamp, and rock gas. Therefore, it is important to be able to detect methane concentration safely and effectively. At present, many models have been proposed to enhance the performance of methane predictions. However, the traditional models displayed inevitable shortcomings in parameter optimization in our experiment, which resulted in their having poor prediction performance. Accordingly, the improved chicken swarm algorithm optimized support vector machine (ICSO-SVM) was proposed to predict the concentration of methane precisely. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum due to its characteristics, so the ICSO algorithm was developed. The formula for position updating of the chicks of the ICSO is not only about the rooster of the same subgroup, but also about the roosters of other subgroups. Therefore, the ICSO algorithm more easily avoids falling into the local extremum. In this paper, the following work has been done. The sample data were obtained by using the methane detection system designed by us; In order to verify the validity of the ICSO algorithm, the ICSO, CSO, genetic algorithm (GA), and particle swarm optimization algorithm (PSO) algorithms were tested, and the four models were applied for methane concentration prediction. The results showed that he ICSO algorithm had the best convergence effect, relative error percentage, and average mean squared error, when the four models were applied to predict methane concentration. The results showed that the average mean squared error values of ICSO-SVM model were smaller than other three models, and that the ICSO-SVM model has better stability, and the average recovery rate of the ICSO-SVM is much closer to 100%. Therefore, the ICSO-SVM model can efficiently predict methane concentration.

scholarly journals Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

2020 ◽  
Vol 5 (3) ◽  
pp. 43-53
Author(s):  
Nor Hayati Binti Shafii ◽  
Rohana Alias ◽  
Nur Fithrinnissaa Zamani ◽  
Nur Fatihah Fauzi
Keyword(s):  
Air Pollution ◽  
Support Vector Machine ◽  
Air Quality ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Pollution Index ◽  
Absolute Error ◽  
Support Vector ◽  
Air Pollution Index ◽  
Squared Error

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development.  Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health.  The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM).  The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.

Rainfall runoff in soil erosion with simulated rainfall, overland flow and cover

Soil Research ◽  
1983 ◽  
Vol 21 (2) ◽  
pp. 109 ◽  
Author(s):  
MJ Singer ◽  
PH Walker
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Overland Flow ◽  
Podzolic Soil ◽  
Simulated Rainfall ◽  
Rainfall Runoff ◽  
Runoff Water ◽  
Soil Transport ◽  
Raindrop Impact

The 20-100 mm portion of a yellow podzolic soil (Albaqualf) from the Ginninderra Experiment Station (A.C.T.) was used in a rainfall simulator and flume facility to elucidate the interactions between raindrop impact, overland water flow and straw cover as they affect soil erosion. A replicated factorial design compared soil loss in splash and runoff from 50 and 100 mm h-1 rainfall, the equivalent of 100 mm h-1 overland flow, and 50 and 100 mm h-1 rainfall plus the equivalent of 100 mm h-' overland flow, all at 0, 40 and 80% straw cover on a 9% slope. As rainfall intensity increased, soil loss in splash and runoff increased. Within cover levels, the effect of added overland flow was to decrease splash but to increase total soil loss. This is due to an interaction between raindrops and runoff which produces a powerful detaching and transporting mechanism within the flow known as rain-flow transportation. Airsplash is reduced, in part, because of the changes in splash characteristics which accompany changes in depths of runoff water. Rain-flow transportation accounted for at least 64% of soil transport in the experiment and airsplash accounted for no more than 25% of soil transport The effects of rainfall, overland flow and cover treatments, rather than being additive, were found to correlate with a natural log transform of the soil loss data.

scholarly journals Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tuan Vu Dinh ◽  
Hieu Nguyen ◽  
Xuan-Linh Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Differential Evolution ◽  
Influencing Factors ◽  
Support Vector ◽  
Adaptive Differential Evolution ◽  
Artificial Neural

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

scholarly journals Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model

2020 ◽  
Vol 14 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Hai-Bang Ly ◽  
Binh Thai Pham
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Liquid Limit ◽  
Support Vector ◽  
Plastic Limit ◽  
Svm Model ◽  
Soil Shear Strength

Background: Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering. Objective: The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit. Methods: An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP). Results: Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength. Conclusion: This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

scholarly journals Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM

2019 ◽  
Vol 8 (2) ◽  
pp. 86 ◽  
Author(s):  
Ping Liu ◽  
Xi Chen
Keyword(s):  
Support Vector Machine ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Support Vector ◽  
Sufficient Information ◽  
Classification Result ◽  
Rotation Forest ◽  
Svm Algorithm ◽  
Multi Temporal ◽  
Occurrence Matrix

Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area.

scholarly journals Analysis of Weather-Type-Induced Soil Erosion in Cultivated and Poorly Managed Abandoned Sloping Vineyards in the Axarquía Region (Málaga, Spain)

2019 ◽  
Vol 12 ◽  
pp. 117862211983940 ◽  
Author(s):  
Jesús Rodrigo-Comino ◽  
José María Senciales ◽  
José Antonio Sillero-Medina ◽  
Yeboah Gyasi-Agyei ◽  
José Damián Ruiz-Sinoga ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Vegetation Cover ◽  
Soil Loss ◽  
Management Practices ◽  
Soil Management ◽  
Weather Conditions ◽  
Surface Flow ◽  
Weather Type ◽  
Rainfall Event ◽  
Management Options

New trends related to market incomes, cultural human development, non-sustainable soil management practices, and climate change are affecting land abandonment in Mediterranean sloping vineyards. It is generally accepted that hydrological processes and, subsequently, soil erosion rates are usually different between cultivated and abandoned soils. However, these alterations are still poorly studied in relation to the general weather conditions in vineyards and abandoned vineyards. Thus, the main goals of this research are to (1) estimate the differences in soil properties, (2) quantify water and soil losses due to rainfall and specific soil management practices, and (3) analyze which kind of weather type and rainfall event is able to generate specific surface flows and soil loss rates. To achieve these goals, we focused on the specific case of the sloping vineyards of the Montes de Málaga (South Spain). We used 4 paired-erosion plots with Gerlach troughs to quantify soil loss and surface flow and conducted an analysis of the weather conditions during each rainfall event. The weather types that generated the highest amount of rainfall in the studied area came from the western (32.6%) and southeast (28.2%) types. The low rainfall events came from the south type (5.9%) and at the 500 hPa level, whereas the rainiest ones came from the southwest (47.7%) and south (34.1%). It is confirmed that there is a bimodality in the rainfall patterns. The results of soil erosion showed that there is a mixed mechanism depending on the state of the soil (vegetation cover, compaction, and initial soil moisture), soil management (tillage, trampling effect, and the use of herbicides). It is observed that the intensity of surface flow is highly correlated to the total rainfall amount and intensity. In the poorly managed abandoned plot, it is important to remark that the effect of tillage in the past, the elimination of the vegetation cover to preserve the soil in bare condition, and its use as a grazing area by cultivating barley highly affects the generation of the highest erosive events. Therefore, it is confirmed that these soil management options are not the most sustainable way to conserve the soil after the abandonment of cultivation.

scholarly journals Effects of Rainfall Intensity and Vegetation Cover on Erosion Characteristics of a Soil Containing Rock Fragments Slope

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Bingqin Zhao ◽  
Lun Zhang ◽  
Zhenyao Xia ◽  
Wennian Xu ◽  
Lu Xia ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Vegetation Cover ◽  
Three Gorges Reservoir ◽  
Rainfall Intensity ◽  
Sediment Load ◽  
Cynodon Dactylon ◽  
Three Gorges ◽  
Rock Fragments ◽  
Runoff Volume ◽  
The Three Gorges

Rainfall events coupled with shallow and gravelly sloping farmland have led to serious soil erosion and associated problems in the Three Gorges reservoir. Previous studies have shown that the use of vegetation is an effective way to control soil erosion. Therefore, an artificial, simulated rainfall experiment study is conducted to determine the effect of rainfall intensity and vegetation cover on runoff volume, sediment load, and runoff hydraulics characteristics. The experiment consists of seven vegetation treatments subjected to three rainfall intensities on a soil that contains rock fragments on a slope of 30°. The results indicate that the runoff volume and sediment load of the bare plot were greater than those of vegetation-covered plots under three different rainfall intensities. When Cynodon dactylon and Indigofera amblyantha were applied together, the plot displayed the best performance for soil loss control, with a reduction of 87.88%–99.11%. According to a redundancy analysis, the change in rainfall intensity had the least impact on the Reynolds number and the runoff volume of the herb-shrub mixed plot in this study. These findings suggest that the effect of combining Cynodon dactylon and Indigofera amblyantha and increasing the vegetation coverage is an effective solution for soil and water loss conservation. The application of this method can alleviate environmental stress on the Three Gorges reservoir.

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