APPLICATION OF MACHINE LEARNING TECHNIQUES TO PREDICT NITROGEN LEVELS IN THE MISSISSIPPI RIVER BASIN

2018 ◽  
Author(s):  
Srinivas Anand ◽  
◽  
Adam S. Ward
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Mississippi River ◽  
Machine Learning Techniques ◽  
Mississippi River Basin ◽  
Nitrogen Levels ◽  
Learning Techniques

Testing different machine learning techniques for runoff routing in a highly glacierized Djankuat river basin (the North Caucasus, Russia).

2021 ◽  
Author(s):  
Ekaterina Rets ◽  
Sergey Fomichev ◽  
Egor Belozerov
Keyword(s):  
Machine Learning ◽  
River Basin ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Water Runoff ◽  
North Caucasus ◽  
Time Step ◽  
The North ◽  
Learning Techniques ◽  
Runoff Routing

<p>Distributed, physically based modelling of runoff routing in highly glacierized river basins is an extremely complicated task as glacier drainage systems functioning is very sophisticated, close to karst river systems but also dynamically developing within very short time periods. Accordingly, runoff routing of glacier melt water is most often based on the concept of linear storage. The number of reservoirs generally vary from 1 to 3. For example, one  ‘fast’ reservoir for melt  and  rain  in  glacierized  grid  cells in GERM model, three parallel different linear reservoirs representing snow, firn and ice in GSM-SOCONT model.</p><p>Here we test applicability of different machine learning techniques (gradient boosting, random forest, LSTM) for runoff routing in a highly glacierized river basin. We use the data from Djankuat alpine research catchment located in the North Caucasus (Russia) for the period of 2007­­-2019. The dataset contains different parameters measured with an hourly or sub-daily time step: water runoff, conductivity, turbidity, temperature, 18O, D content at the main gauging station; measurements of precipitation amount, standard meteorological parameters and radiation fluxes. Results of snow and ice melting modelling in the Djankuat river basin over a regular net with an hourly time step using energy-balance distributed A-Melt model are also used as input data.</p><p>Total runoff from the Djankuat river basin (1) and meltwater runoff according to isotopic hydrograph separation (2) were chosen as target functions. Different sets of features to predict the target functions were generated from the original time series using different combinations of the input parameters as well as variable lag times. To score different machine learning techniques and sets of features to predict target function we use correlation coefficient, Nash-Sutcliff efficiency index (NSE), root mean square error (RMSE).</p><p>The study was supported by the Russian Foundation for Basic Research, grant No. 20-35-70024</p>

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Sign in / Sign up

Export Citation Format

Share Document