scholarly journals Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

2019 ◽  
Vol 11 (24) ◽  
pp. 7212 ◽  
Author(s):  
Joo Hyun Bae ◽  
Jeongho Han ◽  
Dongjun Lee ◽  
Jae E Yang ◽  
Jonggun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Trapping Efficiency ◽  
Learning Models ◽  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips ◽  
Physically Based ◽  
Physically Based Models ◽  
Machine Learning Models

The South Korean government has recently focused on environmental protection efforts to improve water quality which has been degraded by nonpoint sources of water pollution from runoff. In order to take care of environmental issues, many physically-based models have been used. However, the physically-based models take a large amount of work to carry out site simulations, and there is a need to find faster and more efficient approaches. For an alternative approach for sediment management using the physically-based models, the machine learning-based models were used for estimating sediment trapping efficiency of vegetative filter strips. The seven nonlinear regression algorithms of machine learning models (e.g., decision tree, multilayer perceptron, k-nearest neighbors, support vector machine, random forest, AdaBoost and gradient boosting) were applied to select the model which best estimates the sediment trapping efficiency of vegetative filter strips. The sediment trapping efficiencies calculated by the machine learning models showed similar results as those of vegetative filter strip modeling system (VFSMOD-W) model. As a result of the accuracy evaluation among the seven machine learning models, the multilayer perceptron model-derived the best fit with VFSMOD-W model. It is expected that the sediment trapping efficiency of the vegetative filter strips in various cases in agricultural fields in South Korea can be predicted easier, faster and accurately by the machine learning models developed in this study. Machine learning models can be used to evaluate sediment trapping efficiency without complicated physically-based model design and high computational cost. Therefore, decision makers can maximize the quality of their outputs by minimizing their efforts in the decision-making process.

scholarly journals Significance Of Multilayer Perceptron Model For Early Detection Of Diabetes Over Ml Methods

2021 ◽  
Vol 23 (08) ◽  
pp. 148-160
Author(s):  
Dr. V.Vasudha Rani ◽  
◽  
Dr. G. Vasavi ◽  
Dr. K.R.N Kiran Kumar ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Predictive Analytics ◽  
Early Stage ◽  
Feature Selection Method ◽  
Health Condition ◽  
Performance Comparison ◽  
Learning Approaches ◽  
Learning Models ◽  
Machine Learning Models

Diabetes is one of the chronicdiseases in the world. Millions of people are suffering with several other health issues caused by diabetes, every year. Diabetes has got three stages such as type2, type1 and insulin. Curing of diabetes disease at later stages is practically difficult. Here in this paper, we proposed a DNN model and its performance comparison with some of the machine learning models to predict the disease at an earlystage based on the current health condition of the patient. An artificial neural network (ANN) is a predictive model designed to work the same way a human brain does and works better with larger datasets. Having the concept of hidden layers, neural networks work better at predictive analytics and can make predictions with more accuracy. Novelty of this work lies in integration of feature selection method used to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. The results achieved using this method and several conventional machines learning approaches such as Logistic Regression, Random Forest Classifier (RFC) are compared. The proposed DNN method is proved to show better accuracy than Machine learning models for early stage detection of diabetes. This paper work is applicable to clinical support as a tool for making predecisions by the doctors and physicians.

An Empirical Model of Sediment Trapping Efficiency by Vegetative Filter Strips in Chinese Northwest

2014 ◽  
Vol 998-999 ◽  
pp. 1405-1409
Author(s):  
Na Deng ◽  
Huai En Li
Keyword(s):  
Empirical Model ◽  
Reduction Rate ◽  
Trapping Efficiency ◽  
Inflow Rate ◽  
Sediment Trapping ◽  
Vegetative Filter Strips ◽  
Filter Strips ◽  
Vegetative Filter Strip ◽  
Content Factor ◽  
Northwest Region

Vegetative filter strip (VFS) is be defined as areas of vegetation designed to remove sediment and other pollutants from surface runoff. Many factors affect the effectiveness of VFS. So the quantitative analysis on relation between effectiveness and influencing factors had been conducted based on the plot experiment data in this paper. Result reveals that the order in impact degree of its factors is: inflow rate factor > width factor > vegetation condition > pollutants concentration in inflow > initial soil water content factor, the relation equation of purification effect and VFS width is the form of logarithm, and the relation equation of concentration reduction rate and inflow rate is the form of power function. Furthermore, a simple empirical model had been developed to predict sediment trapping efficiency in allusion to Chinese northwest region, which can provide computational basis for design of VFS in northwest region and other similar areas.

scholarly journals Machine Learning in Assessing the Performance of Hydrological Models

Hydrology ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Evangelos Rozos ◽  
Panayiotis Dimitriadis ◽  
Vasilis Bellos
Keyword(s):  
Machine Learning ◽  
Hydrological Models ◽  
Learning Models ◽  
Technological Field ◽  
Physically Based ◽  
Complex Models ◽  
Short Term Forecasting ◽  
Applications Of Machine Learning ◽  
Significant Effort ◽  
Machine Learning Models

Machine learning has been employed successfully as a tool virtually in every scientific and technological field. In hydrology, machine learning models first appeared as simple feed-forward networks that were used for short-term forecasting, and have evolved into complex models that can take into account even the static features of catchments, imitating the hydrological experience. Recent studies have found machine learning models to be robust and efficient, frequently outperforming the standard hydrological models (both conceptual and physically based). However, and despite some recent efforts, the results of the machine learning models require significant effort to interpret and derive inferences. Furthermore, all successful applications of machine learning in hydrology are based on networks of fairly complex topology that require significant computational power and CPU time to train. For these reasons, the value of the standard hydrological models remains indisputable. In this study, we suggest employing machine learning models not as a substitute for hydrological models, but as an independent tool to assess their performance. We argue that this approach can help to unveil the anomalies in catchment data that do not fit in the employed hydrological model structure or configuration, and to deal with them without compromising the understanding of the underlying physical processes.

scholarly journals Graduate Admission Prediction Using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Multilayer Perceptron ◽  
Nearest Neighbor ◽  
Educational Institutions ◽  
Master's Program ◽  
Learning Models ◽  
K Nearest Neighbor ◽  
Machine Learning Models

Student admission problem is very important in educational institutions. This paper addresses machine learning models to predict the chance of a student to be admitted to a master’s program. This will assist students to know in advance if they have a chance to get accepted. The machine learning models are multiple linear regression, k-nearest neighbor, random forest, and Multilayer Perceptron. Experiments show that the Multilayer Perceptron model surpasses other models.

Forecasting of COVID-19 Cases in India: A Predictive Study

2020 ◽  
Vol 16 (1) ◽  
pp. 58-63
Author(s):  
Pooja Sharma ◽  
Tanu Sharma ◽  
Karan Veer
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multilayer Perceptron ◽  
Tamil Nadu ◽  
Sequential Minimal Optimization ◽  
Learning Models ◽  
The World ◽  
Geographic Regions ◽  
Predictive Study ◽  
Machine Learning Models

An outbreak of new coronavirus (COVID-19) originated by SARS-CoV has reached 212 countries throughout the world. India is the second-highest populated country, so it is critical to forecasting the confirmed cases and deaths due to pandemic. To fulfil the purpose, three machine learning models Linear Regression, Multilayer Perceptron, and Sequential Minimal Optimization Regression are used. The predictive data of three geographic regions (India, Maharashtra, and Tamil Nadu) are compared with the data considered to be adequate in practice. The analysis concluded that Sequential Minimal Optimization Regression can be adopted for possible pandemic predictions such as COVID-19.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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