Power Asymmetries, Migrant Agricultural Labour, and Adaptation Governance in Turkey

Turhan, Ethemcan; Kallis, Giorgos; Zografos, Christos

doi:10.1002/9781119383567.ch18

Cited by 1 publication

(1 citation statement)

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“…In this study, two flow measurement stations representing various hydrological conditions of the Seyhan River Basin were established to validate the predictive capacity of the generated model. They were selected in accordance with the conditions of being on various branches of the Seyhan River Basin, as shown in Figure 1 [47]. Moreover, the daily flow measurement stations (FMSs) were used to gather long-term 10-year streamflow data.…”

Section: Datasets and Pre-processingmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

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Section: Datasets and Pre-processingmentioning

confidence: 99%