Alternative solutions for long missing streamflow data for sustainable water resources management

Mesta, Buket; Akgun, Omer Burak; Kentel, Elçin

doi:10.1080/07900627.2020.1799763

Cited by 8 publications

(5 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e sample mean or subgroup mean can be used to fill in missing values in daily streamflow data. However, replacing missing values with sample means may cause underestimating variance and incorrect subgroup identification [35]. Instead, the linear interpolation method is quick and straightforward to use, and it may be sufficient for data with a small gap [36].…”

Section: Datamentioning

confidence: 99%

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

Wegayehu

Muluneh

2022

Advances in Meteorology

View full text Add to dashboard Cite

Hydrological forecasting is one of the key research areas in hydrology. Innovative forecasting tools will reform water resources management systems, flood early warning mechanisms, and agricultural and hydropower management schemes. Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting. The analysis used daily time series data collected from Borkena (in Awash river basin) and Gummera (in Abay river basin) streamflow stations. All data sets passed through rigorous quality control processes, and null values were filled using linear interpolation. A partial autocorrelation was also applied to select the appropriate time lag for input series generation. Then, the data is split into training and testing datasets using a ratio of 80 : 20, respectively. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) were used to evaluate the performance of the proposed models. Finally, the findings are summarized in model variability, lag time variability, and time series characteristic themes. As a result, time series characteristics (climatic variability) had a more significant impact on streamflow forecasting performance than input lagged time steps and deep learning model architecture variations. Thus, Borkena’s river catchment forecasting result is more accurate than Gummera’s catchment forecasting result, with RMSE, MAE, MAPE, and R2 values ranging between (0.81 to 1.53, 0.29 to 0.96, 0.16 to 1.72, 0.96 to 0.99) and (17.43 to 17.99, 7.76 to 10.54, 0.16 to 1.03, 0.89 to 0.90) for both catchments, respectively. Although the performance is dependent on lag time variations, MLP and GRU outperform S-LSTM and Bi-LSTM on a nearly equal basis.

show abstract

Section: Datamentioning

confidence: 99%

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

Wegayehu

Muluneh

2022

Advances in Meteorology

View full text Add to dashboard Cite

show abstract

“…After obtaining fuzzy rules, well-known TS FRB [29] model is constructed and parameters are optimized by recursive least square estimation. For further details, the reader may refer to [28,30].…”

Section: Takagi-sugeno Fuzzy Rule-based Modelmentioning

confidence: 99%

Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model

Akgun

Kentel

2021

The 7th International Conference on Time Series and Forecasting

Self Cite

View full text Add to dashboard Cite

In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the CORDEX database, as inputs to generate ensembled precipitation time series for two meteorological stations (MSs) in the Mediterranean region of Turkey. For each MS, RCM data that are available at the closest grid to the corresponding MSs are used. To generate the fuzzy rules of the TS FRB model, the subtractive clustering algorithm (SC) is utilized. Together with the TS FRB, the simple ensemble mean approach is also applied, and the performances of these two model results and individual RCM predictions are compared. The results show that ensembled models outperform individual RCMs, for monthly precipitation, for both MSs. On the other hand, although ensemble models capture the general trend in the observations, they underestimate the peak precipitation events.

show abstract

“…López-Ruiz et al (2020) discuss the acceptance of direct potable water reuse for domestic purposes from southern Spain. Mesta et al (2020) suggest alternative solutions for a long period of missing streamflow data, and Annys et al (2020) outline Ethiopian experience in expanding small-scale irrigation expansion along the Tekeze River. McNamara et al (2020) outline how models were effectively used to plan irrigation development in a drought-prone region of southern Chile.…”

Section: Future Of Urban Water and Wastewater Management: Views On Singapore International Water Weekmentioning

confidence: 99%