Tercan, Bahadır; Çelik, İbrahim Halil; Yılmaz, Gürkan; Usta, Galip

Earthquakes have been one of the most significant natural hazards threatening humanity throughout history. In this study, we aimed to develop an earthquake safety culture scale to psychometrically measure individuals' attitudes and behaviors regarding their personal life safety before and after an earthquake. During the scale development phase, two different surveys were administered for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The study population consisted of individuals aged 18 and over living in Türkiye. 445 Individuals participated in the EFA and 309 in the CFA. The earthquake safety culture scale, a two-factor structure ("pre-earthquake" and "during and after earthquake"), yielded highly reliable and valid results. It is believed that the resulting scale will contribute to disaster managers in the process of building an earthquake-resilient society, as well as to local and national disaster management policies such as training, awareness, planning, and exercises for community-supported disaster management. It is recommended that this developed scale be retested in different cultures and its relationship with various variables that may influence "pre-earthquake" and "during and after earthquake" attitudes regarding disaster management be investigated.

Kartal, Veysi; Karakoyun, Erkan; Akiner, Muhammed Ernur; Katipoğlu, Okan Mert; Kuriqi, Alban

The models used in this study make it possible to make more accurate predictions about river discharge. These results can influence flood protection strategies, water resource management, and hydropower generation. Due to their ability to capture the underlying temporal relationships in the data, time series forecasts have become increasingly popular in recent years. This study examines the critical processes in river forecasting for the Kizilirmak River basin. We begin with a look at data collection and preparation, followed by an overview of time series forecasting models. Finally, we look at the process of model testing and selection. Seven techniques were used to predict streamflow from meteorological data: Artificial Neural Network (ANN), Firefly-based ANN (FFA-ANN), Random Forest (RF), K-Nearest Neighbors (KNN), Generalized Linear Regression (GLR), Support Vector Machines (SVM), Least Squares Boosted Trees (LSBT). The performance of the models was evaluated using the statistical indicators. The LSBT, RF, and ANN models provided the best results for Kayseri, Kırşehir, and Gemerek stations, respectively. The RF, ANN and GLR models provided second best results for these stations, respectively.

Berhail, Sabri; Katipoğlu, Okan Mert

This study deals with climate variability issues in the Kebir Rhumel basin, situated in northeastern Algeria. This region is experiencing significant changes in climate parameters, which will affect the region’s water resources, agriculture, and overall ecosystem health. The main goal of this study is to examine the severity and frequency of drought from 1981 to 2019. It used the Standard Precipitation-Temperature Index (SPTI), which was recently developed, and clustering methodologies such as c-means and k-means to examine the variation in rainfall patterns characterized by intervals of intense drought and heavy precipitation. These methods help assess the impact of these disturbances in water availability, which result in challenges for agricultural productivity and water management. Additionally, this study points out the significant trend in temperatures over the reported timeline, which aligns with global warming patterns; this intensifies the effect of drought and impacts the region’s water resources. The southeastern and western parts of the basin are areas that experience continuous extreme drought, and different levels of severe drought have been observed over decades. When comparing the results with existing methods, this research has revealed unique climatic trends in this basin area through clustering techniques applied to SPTI results. In particular, 45% of the stations indicate moderate wetness, 35% of the stations experience extreme wetness, and 20% of the stations are extremely dry. Each of these climatic patterns poses distinct challenges for water resource management. These findings demonstrate the complex interplay between rainfall variability, temperature increases, and drought severity. They underscore the urgent need for adaptive water management strategies and plans to address climate change impacts.

Katipoğlu, Okan Mert

Accurate prediction of droughts is vital for effectively managing droughts, assessing drought risks and impacts, drought early warning systems, drought preparedness, and mitigation policies. This study integrated the extreme learning machines algorithm into the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) techniques to predict 1-month lead time meteorological and hydrological droughts. Standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) values were calculated as meteorological and hydrological drought indicators, respectively. The effects of the previous SRI and SPEI values were evaluated to estimate 3- and 12-month SRI-based hydrological droughts. The previous SPEI values were used to estimate SPEI values. The cross-correlation matrix and partial correlation function were used to determine the model input combinations. It is recommended to input delayed drought indices up to 3 months as input to the model for predicting droughts with a lead time of 1 month. The performance of the models was compared with statistical indicators such as coefficient of determination, mean square error, and mean absolute error, and scatter diagram and violin box plot. As a result of the analyses, it was determined that decomposition techniques improved the drought prediction accuracy of the extreme learning machines (ELM) model. The highest prediction performance (R 2: 0.926, MSE: 0.084, and MAE: 0.229) was achieved in the prediction of SRI12 (t + 1) values using the VMD-ELM hybrid approach with the input combination of SRI12 (t) and SRI12 (t − 1) values at the Hinis station. In addition, it has been revealed that the VMD decomposition method provides more successful separation than CEEMDAN in both SRI and SPEI estimations. The study outputs are essential in managing water resources, hydroelectric energy production, sizing of water structures, and transboundary waters.

Akar, Alper; Akar, Özlem; Konakoğlu, Berkant

The selection of emergency assembly points within the urban area holds significant importance for ensuring swift and effective intervention in the aftermath of a potential disaster until temporary shelter areas are prepared. In both historical and instrumental periods, Erzincan (Türkiye) has experienced major earthquakes resulting in significant loss of life and property. The likelihood of similar earthquakes occurring in Erzincan in the future remains high. Therefore, it is necessary to identify safe areas where people can assemble after an earthquake, moving away from hazardous zones. The aim of this study is to analyse the capacities and adequacies of emergency assembly points, which constitute a step in disaster management and enhance the earthquake resilience of the city, using Geographic Information Systems (GIS) based on selected criteria (accessibility, spatial distribution, proximity to healthcare facilities, size, capacity adequacy, and proximity to fault avoidance zones), and to propose solutions. The assessment revealed that the available assembly points are not homogeneously distributed throughout the city and are inadequate to meet the available building and population density. Therefore, in addition to the available 38 emergency assembly points, 67 new alternative emergency assembly points have been identified. The proposed emergency assembly points (in the 75–100% range) increased the number of neighborhoods within the 250 m service area by 43.75% and 42.5% for 500 m.The findings underscore the necessity for more comprehensive and effective planning for Erzincan in the event of a potential disaster or emergency.

Achite, Mohammed; Bazrafshan, Ommolbanin; Katipoğlu, Okan Mert; Azhdari, Zahra

Determining drought indices and characteristics in Algeria is crucial because droughts significantly impact water resources and agricultural production. Additionally, identifying the most suitable drought indicator for the region facilitates effective monitoring of droughts. The study’s main objective is to compare hydro-meteorological droughts, determine their distribution, and assess drought risk. Various drought indices, which are continuous functions of rainfall and other hydro-meteorological variables, are typically used for this purpose. This study calculates seven indices' effectiveness for drought monitoring and assessment in Algeria's Wadi Ouhrane basin. For this purpose, the temporal variation of drought indicators, distribution graphs, weighted Cohen's kappa (Kw), and correlation coefficient values was compared. Among the indices used in the study, four indices, namely SPI, CZI, ZSI, and MCZI, showed high similarity in their behavior. As the time scale increases from 1 to 24 months, the correlation coefficient exceeds 0.97, and Kw becomes greater than 0.57. Furthermore, there is a weak correlation (R < 0.4) between the meteorological and hydrological-based SRI indicators, and the highest correlation was found between the RDI and SRI indices. Therefore, these indices indicate that the precipitation and ET (temperature) ratio is more suitable for hydrological drought studies.

Sarıgöl, Metin; Katipoğlu, Okan Mert

Flood routing calculations are of vital importance in estimating the floods occurring in the downstream region and taking all necessary measures to minimize the damages that the flood may cause. The prediction success of the created algorithms in the flood routing analysis was tried to be measured by training the daily flood data with machine learning algorithms. This research trained the daily average flood hydrograph data with regression tree, Gaussian process regression, support vector machine, ANFIS and regression tree ensembles. Then the flood routing calculations were made using these models for the hourly flood hydrograph of 2015. The success of the created machine learning algorithms was compared with statistical criteria such as mean absolute error, the root of mean squares of errors and coefficient of determination (R 2), and visually with Taylor diagrams and boxplots. As a result of the analysis established, the ANFIS model divided the inputs into 5 subsets, and the trim membership function showed good results in flood routing. It has been proved that with limited flow data, floods can be postponed, and loss of life and property can be prevented.