Spatiotemporal analysis of droughts in Hirfanli Dam basin, Turkey by the Standardised Precipitation Evapotranspiration Index (SPEI)

ZEYBEKOĞLU, Utku

doi:10.1007/s11600-021-00719-x

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…Simsek [40] designed a hydrological drought analysis using the Streamflow Drought Index (SDI) method for the Seyhan River Basin. Additionally, Zeybekoglu [41] applied the Standardized Precipitation Evapotranspiration Index (SPEI) for the first time in the Seyhan River Basin. Zerberg and Ozkaya developed a daily reservoir operation model to evaluate the water storage changes in the Seyhan River Basin.…”

Section: Study Regionmentioning

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: Study Regionmentioning

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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“…In this study, Hirfanli Dam basin, which is located in the semi-arid climate region where climate change can be seen due to its location, was chosen as the study area. The drought situation in the basin and the Central Anatolia region, where it is geographically located, has been examined with different methods in the literature [37][38][39][40][41][42]. Temperature and precipitation series analyzes were made by using Mann Kendall (MK), Spearman's Rho (SR) and Innovative Trend Analysis (ITA).…”

Section: Introductionmentioning

confidence: 99%

Analysis of temperature and precipitation series of Hirfanli Dam Basin by Mann Kendall, Spearman’s Rho and Innovative Trend Analysis

GÜNDÜZ,

ZEYBEKOĞLU

2024

Turkish Journal of Engineering

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In this study long-term trend analysis of precipitation and temperature series are determined in the Hirfanli dam basin of Turkey. Data is obtained from the Turkish State Meteorological Service for the period of 1968 to 2017 for Gemerek, Kayseri, Kirsehir, Nevsehir, Sivas and Zara throughout the press. Mann-Kendall, Spearman's Rho and Innovative Trend Analysis are used for trend analysis with 95% confidence levels. According to the results of the temperature series upward trend were determined. The results of all methods are similar but increasing significant trends were determined by Mann Kendall and Spearman's Rho except Zara. According to the temperature series results, with decreasing trends in Gemerek, Kirsehir, Nevsehir and Zara; Increasing trends were determined in Kayseri and Sivas. The results of Mann Kendall and Spearman's Rho methods show parallelism with each other. Contrary to other methods, Innovative Trend Analysis determined a decreasing trend in Kayseri. As a result of the analysis, the trends in the Temperature series are not significant at the 95% confidence level.

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“…According to the result, it required that temperature should be considered in future studies. Zeybekoglu (2022) conducted a drought using SPEI on Hirfanli Dam Basin with metrological data including the precipitation and temperature data obtained from Gemerek, Kayseri, Kirsehir, Nevsehir, Sivas, and Zara meteorology observation stations located in Hirfanli Dam Basin between 1968 and 2017. It was reported by Zeybekoglu that the most severe drought happened in 2001 in Hirfanlı Dam basin.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

Tuğrul,

HINIS

2024

Preprint

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Defined as a decrease in average rainfall amounts, drought is one of the most insidious natural disasters. When it starts, people may not be aware of it, that's why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods such as drought indices one of which Standardized Precipitation Index (SPI) is. In this study, SPI to detect droughts and machine learning algorithms, support vector machines (SVM), artificial neural networks (ANN), random forest (RF), decision tree (DT), frequently used in the literature to predict droughts and 3 different statistical methods: correlation coefficient (r), Root Mean-square Error (RMSE), Nash-Sutcliffe Efficiency (NSE) Coefficient to investigate model performance values were used. Wavelet analysis was also applied to improve model performances. Konya closed basin located in the middle of Türkiye in terms of location and is among the leading regions of Turkey in terms of grain is one of the regions most affected by droughts in Türkiye. One of the most important water resources of the region is the Apa dam. It provides water to many fields which fertile land in its vicinity and is affected by droughts. Therefore, this region was determined as the study area. Meteorological data, total monthly precipitation, that could represent the region were obtained between 1955 and 2020 from general directorate of state water works and general directorate of meteorology. The results show that among the models analyzed with machine learning algorithms, the best results were obtained from M04 model whose input structure was created from SPI, different times steps, data delayed up to 5 months and total monthly precipitation data for time t-1. Among machine learning algorithms, SVM has achieved the most successful results in not only without wavelet transform (WT) but also with WT. Effective results were obtained from M04 in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

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Spatiotemporal analysis of droughts in Hirfanli Dam basin, Turkey by the Standardised Precipitation Evapotranspiration Index (SPEI)

Cited by 10 publications

References 48 publications

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

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

Analysis of temperature and precipitation series of Hirfanli Dam Basin by Mann Kendall, Spearman’s Rho and Innovative Trend Analysis

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

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