Two-step Daily Reservoir Inflow Prediction Using ARIMA-Machine Learning and Ensemble Models

2020 ◽  
Author(s):  
Akshita Gupta ◽  
Arun Kumar
Keyword(s):  
Machine Learning ◽  
Ensemble Models ◽  
Reservoir Inflow ◽  
Inflow Prediction

scholarly journals Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs

2020 ◽  
Vol 287 (1920) ◽  
pp. 20192882 ◽  
Author(s):  
Maya Wardeh ◽  
Kieran J. Sharkey ◽  
Matthew Baylis
Keyword(s):  
Machine Learning ◽  
Human Health ◽  
Well Being ◽  
Pathogen Transmission ◽  
Learning Approaches ◽  
Economic Stability ◽  
Ensemble Models ◽  
Network Characteristics ◽  
Key Factor ◽  
Key Aspects

Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, particularly where research and resources are limited. Here, we integrate complex networks and machine learning approaches to develop a new approach to identifying reservoirs. An exhaustive dataset of mammal–pathogen interactions was transformed into networks where hosts are linked via their shared pathogens. We present a methodology for identifying important and influential hosts in these networks. Ensemble models linking network characteristics with phylogeny and life-history traits are then employed to predict those key hosts and quantify the roles they undertake in pathogen transmission. Our models reveal drivers explaining host importance and demonstrate how these drivers vary by pathogen taxa. Host importance is further integrated into ensemble models to predict reservoirs of zoonoses of various pathogen taxa and quantify the extent of pathogen sharing between humans and mammals. We establish predictors of reservoirs of zoonoses, showcasing host influence to be a key factor in determining these reservoirs. Finally, we provide new insight into the determinants of zoonosis-sharing, and contrast these determinants across major pathogen taxa.

scholarly journals Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7834
Author(s):  
Christopher Hecht ◽  
Jan Figgener ◽  
Dirk Uwe Sauer
Keyword(s):  
Machine Learning ◽  
Binary Data ◽  
Training Data ◽  
Gradient Boosting ◽  
Traffic Density ◽  
Learning Models ◽  
Charging Infrastructure ◽  
Ensemble Models ◽  
Charging Station ◽  
Machine Learning Models

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction

2020 ◽  
Vol 34 (4) ◽  
pp. 1479-1493 ◽  
Author(s):  
Xiaoli Zhang ◽  
Haixia Wang ◽  
Anbang Peng ◽  
Wenchuan Wang ◽  
Baojian Li ◽  
...  
Keyword(s):  
Data Driven ◽  
Reservoir Inflow ◽  
Inflow Prediction

scholarly journals The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2559 ◽  
Author(s):  
Celestine Iwendi ◽  
Suleman Khan ◽  
Joseph Henry Anajemba ◽  
Mohit Mittal ◽  
Mamdouh Alenezi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Ensemble Models ◽  
Detection Systems

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

scholarly journals Keakuratan Prediksi Inflow Waduk Dengan Neraca Air Waduk

Teknik ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 94
Author(s):  
Dyah Ari Wulandari ◽  
Hary Budieny ◽  
Dwi Kurniani
Keyword(s):  
Water Balance ◽  
Reservoir Operation ◽  
Human Error ◽  
Series Data ◽  
Data Series ◽  
Reservoir Water ◽  
Balance Accuracy ◽  
Error Factor ◽  
Reservoir Inflow ◽  
Inflow Prediction

Dalam perhitungan inflow waduk sering digunakan persamaan neraca air waduk yang menggunakan data seri laporan harian operasi waduk, evaporasi dan curah hujan diwaduk, dan lengkung H-V-A waduk. Pada pengamatan data series laporan harian operasi waduk dan pengukuran kapasitas tampungan waduk, dapat terjadi kesalahan yang disebabkan karena kesalahan faktor manusia maupun faktor alat, hal ini akan menyebabkan kesalahan pula pada besarnya inflow waduk yang dihasilkan. Lebih lanjut di dalam perencanaan, data series inflow waduk ini diperlukan sebagai input pada pemodelan optimasi operasi waduk dan sedimentasi waduk, sehingga keakuratan datanya sangat diperlukan. Tujuan penelitian ini adalah untuk mengevaluasi tingkat akurasi penggunaan neraca air waduk dalam memprediksi inflow waduk. Untuk mengetahui tingkat akurasi dilakukan dengan membandingkan antara inflow waduk dari anak sungai hasil pengukuran dan hasil hitungan dengan persamaan neraca air waduk. Kemudian dilakukan variasi periode pengukuran dan kurva H- V-A yang digunakan. Berdasarkan penelitian yang dilakukan maka pada periode perhitungan yang lebih lama menghasilkan tingkat error yang lebih kecil. Pemakaian kurva waduk yang berbeda menghasilkan inflow yang berbeda. Tingkat error yang didapat masih cukup besar, diatas 30 %, sehingga perhitungan inflow waduk dari anak sungai dengan menggunakan metode neraca air waduk kurang akurat. [Title: Accuracy of Reservoir Inflow Prediction Using Reservoir Water Balance] In the calculation of reservoir inflow often used reservoir water balance equation using the data series of daily reports reservoir operation, evaporation and precipitation, and H-V-A curve. In observation of the data series of daily reports of reservoir operation and measurement of reservoir storage capacity, the errors may occur due to human error factor and factor appliance. This will cause an error on the reservoir inflow generated. Further, in the planning, this series data of reservoir inflow is required as input to the modeling of reservoir operation optimization and reservoir sedimentation, so the accuracy of the data are required. The purpose of this study was to evaluate the use of the reservoir water balance accuracy rate in predicting inflow. To determine the level of accuracy, the effort is done by comparing the inflow tributary reservoirs of measurement and the count with the reservoir water balance. Then perform variations of the measurement period and curves H-V-A is used. Based on the research conducted in the period longer calculation produces a smaller error. The different H-V-A curve results in the different inflow. Error rate obtained is still quite large, above 30%, so the calculation of tributary inflow reservoirs using reservoir water balance method is less accurate.  

Novel Machine Learning Ensemble Models for Active Diabetes Diagnosis

2021 ◽  
Author(s):  
Mohamed A.M. Iesa ◽  
Abhinandan P Shirahatt ◽  
Harsha Sharma ◽  
Mohit Kumar Goyal ◽  
Amit Shrivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diabetes Diagnosis ◽  
Ensemble Models

Forecasting of Reservoir Inflow Using Machine Learning—Case Study: Klang Gate Dam Reservoir

2021 ◽  
pp. 33-47
Author(s):  
Karim Sherif Mostafa Hassan ◽  
Yuk Feng Huang ◽  
Chai Hoon Koo ◽  
Tan Kok Weng ◽  
Ali Najah Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dam Reservoir ◽  
Reservoir Inflow

Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

2021 ◽  
pp. 693-709
Author(s):  
Wasiur Rhmann ◽  
Gufran Ahmad Ansari
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Software Testing ◽  
Prediction Models ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Software Fault Prediction ◽  
Ensemble Models ◽  
Software Fault

Software engineering repositories have been attracted by researchers to mine useful information about the different quality attributes of the software. These repositories have been helpful to software professionals to efficiently allocate various resources in the life cycle of software development. Software fault prediction is a quality assurance activity. In fault prediction, software faults are predicted before actual software testing. As exhaustive software testing is impossible, the use of software fault prediction models can help the proper allocation of testing resources. Various machine learning techniques have been applied to create software fault prediction models. In this study, ensemble models are used for software fault prediction. Change metrics-based data are collected for an open-source android project from GIT repository and code-based metrics data are obtained from PROMISE data repository and datasets kc1, kc2, cm1, and pc1 are used for experimental purpose. Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms. Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.

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