scholarly journals On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ma Shaofu ◽  
Anas M. Al-Juboori ◽  
Asmaa Hussein Alwan ◽  
Abdel-Salam G. Abdel-Salam
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
River Flow ◽  
Gene Expression Programming ◽  
Annual Streamflow ◽  
The North ◽  
Actual Mechanism ◽  
Monthly Streamflow ◽  
Additive Regression ◽  
Diyala River

Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. The modeling results indicated an optimistic simulation for generating the monthly streamflow time series from annual streamflow time series. The potential of the MART model was superior to the GMDH and GEP models for Upper Zab River (R2 0.84, 0.64, and 0.47), Lower Zab River (R2 0.75, 0.46, and 0.40), and Diyala River (R2 0.78, 0.42, and 0.5). The results of RMSE were 113, 169, and 208 for Upper Zab River, 95, 149, and 0.5 for Lower Zab River, and 73, 118, and 109 for Diyala River. The results have proved the possibility of changing the timescale in generating streamflow data.

Data mining process for modeling hydrological time series

2012 ◽  
Vol 44 (1) ◽  
pp. 78-88 ◽  
Author(s):  
M. Erol Keskin ◽  
Dilek Taylan ◽  
Ecir Ugur Kucuksille
Keyword(s):  
Data Mining ◽  
Time Series ◽  
River Flow ◽  
Markov Models ◽  
Electrical Power ◽  
Streamflow Prediction ◽  
Stream Flows ◽  
Power Resources ◽  
Monthly Streamflow ◽  
Using Data

The main purpose of this study was to develop an optimum flow prediction model, based on data mining process. The data mining process was applied to predict river flow of Seyhan Stream in the southern part of Turkey. Hydrological time series modeling was applied using monthly historical flow records to predict Seyhan Stream flows. Seyhan Stream flows were modeled by Markov models and it was seen that it adapted AR(2). Hence, Ft–2 and Ft–1 flows in (t–2) and (t–1) months were the taken inputs. For monthly streamflow predictions, data were taken from the General Directorate of Electrical Power Resources Survey and Development Administration. Used data covered 35 years between 1969 and 2003 for monthly streamflows. Furthermore, for the effect of monthly periodicity in hydrological time series cos (2πi/12), sin (2πi/12) (I = 1, 2,…, 12) were included as inputs. Then, Ft flows in (t) months were modeled by data mining process. It was concluded that with using data mining process for streamflow prediction, it was possible to estimate missing or unmeasured data.

Newly explored machine learning model for river flow time series forecasting at Mary River, Australia

2020 ◽  
Vol 192 (12) ◽  
Author(s):  
Fang Cui ◽  
Sinan Q. Salih ◽  
Bahram Choubin ◽  
Suraj Kumar Bhagat ◽  
Pijush Samui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
River Flow ◽  
Learning Model ◽  
Flow Time ◽  
Time Series Forecasting ◽  
Machine Learning Model

scholarly journals Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising

2017 ◽  
Vol 49 (3) ◽  
pp. 711-723 ◽  
Author(s):  
Xiaorong Lu ◽  
Xuelei Wang ◽  
Liang Zhang ◽  
Ting Zhang ◽  
Chao Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Wavelet Decomposition ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Gene Expression Programming ◽  
Stationary Time Series ◽  
Series Data ◽  
Effective Measure

Abstract Due to the effects of anthropogenic activities and natural climate change, streamflows of rivers have gradually decreased. In order to maintain reliable water supplies, reservoir operation and water resource management, accurate streamflow forecasts are very important. Based on monthly flow data from five hydrological stations in the middle and lower parts of the Hanjiang River Basin, between 1989 and 2009, we consider an efficient approach of adopting the gene expression programming model based on wavelet decomposition and de-noising (WDDGEP) to forecast river flow. Original flow time series data are initially decomposed into one sub-signal approximation and seven sub-signal details using the dmey wavelet. A wavelet threshold de-noising method is also applied in this study. Data that have been de-noised after decomposition are then adopted as inputs for WDDGEP models. Finally, the forecasted sub-signal results are summed to formulate an ensemble forecast for the original monthly flow series. A comparison of the prediction accuracy between the two models is based on three performance evaluation measures. Results show that the new WDDGEP models can effectively enhance accuracy in forecasting streamflow, and the proposed wavelet-based de-noising of the observed non-stationary time series is an effective measure to improve simulation accuracy.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Sources of Chlorophenolic Compounds to the Fraser River Estuary

1988 ◽  
Vol 23 (1) ◽  
pp. 55-68 ◽  
Author(s):  
J. H. Carey ◽  
J. H. Hart
Keyword(s):  
River Flow ◽  
River Estuary ◽  
Low Flow ◽  
Fraser River ◽  
Pulp Mills ◽  
Flow Conditions ◽  
Downstream Site ◽  
Upstream Site ◽  
The North ◽  
Main River

Abstract The identity and concentrations of chlorophenolic compounds in the Fraser River estuary were determined under conditions of high and low river flow at three sites: a site upstream from the trifurcation and at downstream sites for each main river arm. Major chlorophenolics present under both flow regimes were 2,4,6-trichlorophenol (2,4,6-TCP), 2,3,4,6-tetrachlorophenol (2,3,4,6-TeCP), pentachlorophenol (PCP), tetrachloroguaiacol (TeCG) and a compound tentatively identified as 3,4,5-trichloroguaiacol (3,4,5-TCG). Under high flow conditions, concentrations of the guaiacols were higher than any of the Chlorophenols and concentrations of all five chlorophenolics appeared to correlate. Under low flow conditions, concentrations of chloroguaiacols were higher than Chlorophenols at the upstream site and at the downstream site on the Main Arm, whereas at the downstream site on the North Arm, concentrations of 2,3,4,6-TeCP and PCP were higher than the chloroguaiacols in some samples. Overall, the results indicate that pulp mills upstream from the estuary are important sources of chlorophenolics to the estuary under all flow conditions. Additional episodic inputs of 2,3,4,6-TeCP and PCP from lumber mills occur along the North Arm. When these inputs occur, they can cause the concentrations of Chlorophenols in the North Arm to exceed provisional objectives. If chloroguaiacols are included as part of the objective, concentrations of total chlorophenolics in water entering the estuary can approach and exceed these objectives, especially under low flow conditions.

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