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 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 311 A brief overview, comparison and practical applications of machine learning models

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 44-45
Author(s):  
Dan Tulpan
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Training Opportunity ◽  
Practical Applications ◽  
Learning Techniques ◽  
Hands On ◽  
Practical Session ◽  
Applications Of Machine Learning ◽  
Machine Learning Models

Abstract This is a hands-on workshop offered as a pre-conference training opportunity for researchers interested in applying machine learning techniques to animal science datasets with the purpose of classifying, clustering, performing linear and non-linear regressions or selecting a subset of features relevant to further studies. The objective of this workshop is to provide the audience with a way to formulate a problem such that it will be solvable by machine learning techniques and apply an exploratory analysis of various machine learning on different datasets. The workshop is structured in a hands-on format and includes a brief overview of basic notions about machine learning, a description of relevant models and evaluation metrics followed by a practical session. The practical session requires each attendee to bring their own laptop and have already installed the Waikato Environment for Knowledge Analysis (Weka) workbench for machine learning available from https://www.cs.waikato.ac.nz/ml/weka/ and all freely available machine learning models. The Weka installation of freely available machine learning models can be achieved by using the Weka Package Manager available from the Tools menu in the main application. Detailed information will be provided 2 weeks before the beginning of the workshop (week of July 5, 2020) at the following URL:http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2020_mlworkshop/

scholarly journals Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3045
Author(s):  
Mudabbir Ali ◽  
Asad Masood Khattak ◽  
Zain Ali ◽  
Bashir Hayat ◽  
Muhammad Idrees ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surrogate Model ◽  
Data Science ◽  
End Users ◽  
Learning Models ◽  
Daily Life Activities ◽  
Unseen Data ◽  
Complex Models ◽  
Interpretable Models ◽  
Machine Learning Models

Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are not agnostic and are computationally expensive. Thus, in this work, we propose a strategy that can tackle the aforementioned issues. A surrogate model assisted us in building interpretable models. Moreover, it helped us achieve results with accuracy close to that of the black box model but with less processing time. Thus, the proposed technique is computationally cheaper than traditional methods. The significance of such a novel technique is that data science developers will not have to perform strenuous hands-on activities to undertake feature engineering tasks and end-users will have the graphical-based explanation of complex models in a comprehensive way—consequently building trust in a machine.

80 A Brief Overview, Comparison and Practical Applications of Machine Learning Models

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 44-44
Author(s):  
Dan Tulpan
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Practical Applications ◽  
Learning Techniques ◽  
Hands On ◽  
Practical Session ◽  
Applications Of Machine Learning ◽  
Machine Learning Models

Abstract This is a hands-on workshop offered as a pre-conference training opportunity for researchers interested in applying machine learning techniques to animal science datasets with the purpose of classifying, clustering, performing linear and non-linear regressions or selecting a subset of features relevant to further studies. The objective of this workshop is to provide the audience with a way to formulate a problem such that it will be solvable by machine learning techniques and apply an exploratory analysis of various machine learning algorithms on different datasets. The workshop is structured in a hands-on format and includes a brief overview of basic notions about machine learning, a description of relevant models and evaluation metrics followed by a practical session. The practical session requires each attendee to bring their own laptop and have already installed the Waikato Environment for Knowledge Analysis (Weka) workbench for machine learning available from https://www.cs.waikato.ac.nz/ml/weka/ and all freely available machine learning models. The Weka installation of freely available machine learning models can be achieved by using the Weka Package Manager available from the Tools menu in the main application. Detailed information will be provided before the beginning of the workshop at the following URL: http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2021_mlworkshop/

scholarly journals Machine Learning Models for Industrial Applications

2021 ◽  
Author(s):  
Enislay Ramentol ◽  
Tomas Olsson ◽  
Shaibal Barua
Keyword(s):  
Machine Learning ◽  
Production Efficiency ◽  
Energy Savings ◽  
Oil And Gas ◽  
Production Quality ◽  
Industrial Applications ◽  
Learning Models ◽  
Efficiency Increase ◽  
Applications Of Machine Learning ◽  
Machine Learning Models

More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions.

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