Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin

Niazkar, Majid; Piraei, Reza; Eryılmaz Türkkan, Gökçen; Hırca, Tuğçe; Gangi, Fabiola; Afzali, Seied Hosein

doi:10.1007/s00704-023-04710-y

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…If n > 10, the S statistic is normally distributed to obtain the standard normal variance (Z) [33]. In addition, the significance level α is utilized to determine if there is a statistically significant change in the data.…”

Section: Trend Analysis Methodsmentioning

confidence: 99%

Investigating Uncertainty of Future Predictions of Temperature and Precipitation in The Kerman Plain under Climate Change Impacts

Goodarzi,

Heydaripour,

Jamali

et al. 2023

Hydrology

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Climate change affects hydroclimatic variables, and assessing the uncertainty in future predictions is crucial. This study aims to explore variations in temperature and precipitation in the Kerman Plain under climate change impacts between 2023 and 2054. For this purpose, two climate models, MRI-ESM-2 and BCC-CSM2-MR, were used to simulate precipitation and temperature under two different scenarios. The Mann–Kendall test was employed to analyze the annual time series in the future period. The results indicated an increase in the average temperature of about 1.5 degrees Celsius based on both scenarios in the coming years. Furthermore, an average annual increase of 6.37 mm of precipitation was predicted under the SSP585 scenario. Meanwhile, under the SSP585 scenario, an increase was estimated using the MRI-ESM-2 model, and a decrease was predicted with the BCC-CSM2-MR model. The Mann–Kendall test revealed a downward trend in the BCC-CSM2-MR model under both scenarios and an upward trend in the MRI-ESM-2 model under both scenarios. The bootstrap method and the R-factor index were exploited in this study with a 95% confidence interval to estimate the uncertainty of the predicted data. The results demonstrated that the predicted precipitation is more uncertain than the temperature. Finally, it is postulated that the obtained results provide necessary information for water resource management under a changing climate in the study area.

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Section: Trend Analysis Methodsmentioning

confidence: 99%

Investigating Uncertainty of Future Predictions of Temperature and Precipitation in The Kerman Plain under Climate Change Impacts

Goodarzi,

Heydaripour,

Jamali

et al. 2023

Hydrology

Self Cite

View full text Add to dashboard Cite

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Assessment of Different Methods for Estimation of Missing Rainfall Data

Hırca,

Eryılmaz Türkkan

2024

Water Resour Manage

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Missing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.

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Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index

Niazkar,

Piraei,

Zakwan

2024

Water Resour Manage

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Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin

Cited by 4 publications

References 37 publications

Investigating Uncertainty of Future Predictions of Temperature and Precipitation in The Kerman Plain under Climate Change Impacts

Investigating Uncertainty of Future Predictions of Temperature and Precipitation in The Kerman Plain under Climate Change Impacts

Assessment of Different Methods for Estimation of Missing Rainfall Data

Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index

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