Yazarlar
- Katipoğlu, Okan Mert(57)
- Akın, Fatih(15)
- Manga, Muge(12)
- Achite, Mohammed(11)
- Balli, Esra(11)
- Kartal, Veysi(10)
- Yilmaz, Harun(10)
- Destek, Mehmet Akif(9)
- Elshaboury, Nehal(8)
- Demirci, Oğuz Kürşat(7)
- Ganjehsarabi, Hadi(7)
- Yilmaz, Ilker(7)
- Cam, Omer(6)
- Cengiz, Orhan(6)
- Dinçer, Selin(6)
- Elbeltagi, Ahmed(6)
- Manga, Müge(6)
- Seyhan, Aslıhan Kurnuç(5)
- Sigeze, Ciler(5)
- Çatık, Abdurrahman Nazif(5)
- Abdallah, Mohammed(4)
- Acar, Reşat(4)
- Ballı, Esra(4)
- Bazrafshan, Ommolbanin(4)
- Berhail, Sabri(4)
- Dogan, Ahmet(4)
- Kilic, Ugur(4)
- Kuziboev, Bekhzod(4)
- Makhmudov, Samariddin(4)
- Mohammadi, Babak(4)
- Pande, Chaitanya Baliram(4)
- Pandey, Kusum(4)
- Sari, İdris(4)
- Sarıgöl, Metin(4)
- Uzun, Sinem(4)
- Çatik, Abdurrahman Nazif(4)
- Acar, Resat(3)
- Bakirci, Kadir(3)
- Dalkilic, Huseyin Yildirim(3)
- Ekici, Selcuk(3)
Yayınlar
- ArticleYalnız künye2024Integration of extreme learning machines with CEEMDAN and VMD techniques in the prediction of the multiscalar standardized runoff index and standardized precipitation evapotranspiration index
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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
Pande, Chaitanya Baliram; Kushwaha, Nand Lal; Alawi, Omer A.; Sammen, Saad Sh; Sidek, Lariyah Mohd; Yaseen, Zaher Mundher; Pal, Subodh Chandra; Katipoğlu, Okan Mert
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Past, present, and future potential distributions of the African multipurpose tree Detarium senegalense (Fabaceae)
Dassou, Gbèwonmèdéa Hospice; Agoundé, Gafarou; Akouété, Pathmos; Favi, Gnimansou Abraham; Kpétikou, Ghyslain Chabi; Salako, Kolawolé Valère; Ouachinou, Jéronime Marie-Ange Sènami; Makponsè, Judicael; Kouyaté, Amadou Malé; Sari, İdris
Background and aims – Climate change induces increasing temperatures and drought, with possible profound shifts in species’ presence and distribution. Ecological niche models are widely used to assess plant species responses to climate change. However, such data are scarce for West Africa, particularly for vulnerable multipurpose species. This study focuses on modelling the ecological niche and the conservation status of the multipurpose tree Detarium senegalense to improve insights into its habitat suitability in West Africa under past, present, and future climatic conditions. This will provide an essential basis for setting up global management plans through efficient conservation and ecological restoration policies. Material and methods – The potential distribution of D. senegalense under past, current, and future climate scenarios were assessed using four algorithms including generalized additive models (GAM), generalized linear models (GLM), random forest (RF), and Maximum Entropy (MaxEnt). We also assessed the shift direction of suitable habitats and the conservation status of the species based on IUCN criteria. Overall, 220 occurrences were combined with a set of five bioclimatic variables to run the models. Key results – Models performed well with good values of AUC (0.92) and TSS (0.73). Isothermality (41.10%) and Precipitation of Wettest Month (21.50%) contributed most to the distribution of the species. The distribution of D. senegalense was relatively constant from the past to the present but could decrease in the next decades. In the future, only 17.70% and 13.98% of the areas were predicted to be suitable under respectively ssp245 and ssp585. In protected areas, the suitable areas under ssp245 were estimated at 21.01% with a decrease of 2.50% and 14.60% with a decrease of 8.61% under ssp585 by 2050. The direction of the distribution shifted to the south-east under future climate scenarios. The conservation status assessment of the species is Least Concern (LC). Conclusion – This study improves our understanding of the past, present-day, and future distribution of the species and provides support to better manage the conservation of D. senegalense in West Africa.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Foreign investments and load capacity factor in BRICS: the moderating role of environmental policy stringency
Yıldırım, Metin; Destek, Mehmet Akif; Manga, Müge
This research examines whether environmental regulations have a moderating effect on the link between foreign direct investment and the environment, as well as the effect of foreign capital investments on environmental quality for BRICS nations. In this approach, using second-generation panel data methodologies for the period 1992–2020, the impacts of foreign direct investments, real national income, consumption of renewable energy, and environmental stringency index on the load capacity factor are explored in the base empirical model. In order to test if there is any evidence of a potential parabolic link between economic growth and environmental quality, the model also includes the square of real national income. In addition, in the robustness model, the moderating role of environmental policy on foreign investment and environmental quality is checked. Empirical results show a U-shaped association between environmental quality and economic development. The usage of renewable energy and the environmental stringency index is also shown to improve environmental quality, although foreign direct investments decrease it. Finally, it is determined that environmental regulations are effective in undoing the negative impacts of foreign capital investments on environmental quality, demonstrating the validity of their moderating function.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024How do energy consumption, globalization, and income inequality affect environmental quality across growth regimes?
Çatık, Abdurrahman Nazif; Bucak, Çağla; Ballı, Esra; Manga, Muge; Destek, Mehmet Akif
This paper investigates the impacts of renewable and nonrenewable energy consumption, income inequality, and globalization on the ecological footprints of 49 countries for the period of 1995–2018. Panel cointegration test reveals a long-run relationship between the variables. Long-run parameter estimates derived from AMG and CCEMG, increasing income and nonrenewable energy consumption, have a significant positive impact on the ecological footprint, while countries that consume more renewable energy have seen an improvement in the quality of the environment. Conversely, neither income inequality nor globalization has a significant effect on national EFs. Evidence from the estimation of the panel threshold error correction model, where GDP growth is used as the transition variable, indicates a significant threshold effect, which supports a nonlinear relationship among the variables by identifying two distinct growth regimes: lower and upper. For the estimation sample, the positive and significant parameter estimates for economic growth in both growth regimes do not support the EKC hypothesis. The results indicate that renewable and nonrenewable energy consumption has a larger impact on the EF in the upper than lower growth regime. The threshold estimates are in line with the linear long-run estimates that do not indicate that income inequality has a significant impact on ecological footprint. However, globalization appears to negatively affect environmental quality in the lower growth regime.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Analyzing the load capacity curve hypothesis for the Turkiye: A perspective for the sustainable environment
Caglar, Abdullah Emre; Destek, Mehmet Akif; Manga, Muge
Although it takes time to achieve a sustainable environment, this goal can be possible with the Sustainable Development Goals. The transition from gray energy sources to green in the 21st century is among the important policy goals of global economies. Since this process is long, countries focus on energy efficiency as part of their carbon reduction policies. In this context, this study investigates the impact of clean energy technologies on environmental quality in Turkiye. Using the load capacity curve (LCC) hypothesis, human capital, income, and natural resources are theoretically added to the balance model. The novel Augmented ARDL with Fourier terms approach is used in the empirical analysis. As a result of the analysis, the LCC hypothesis is valid, and income will support environmental quality after a certain level. While human capital increases environmental quality, natural resources bring environmental pollution. Finally, it is determined that clean energy technologies have not yet reached the level that enables carbon reduction. The finding that clean energy technologies are insufficient for Turkiye, as a result of empirical analysis, shows the uniqueness of this study. With our new findings, policymakers can take action for a sustainable environment. Since Turkiye has to ensure energy security due to its regional characteristics, it should increase investments in clean energy technologies. Based on empirical findings, clean energy technologies will gain momentum as policymakers support human capital.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2024A Case Study on Investigating Probabilistic Characteristics of Wind Speed Data for Green Airport
Tatli, Ali; Suzer, Ahmet Esat; Filik, Tansu; Karakoc, Tahir Hikmet
Accurate estimation of wind distribution plays a vital role in modelling more efficiently and minimizing theoretical computation errors in terms of practical applications. This research strives to conduct elaborately an assessment of the wind behavior and features of an international airport based on various heights from the ground level. Wind speed at each height, composed of 10 m, 20 m, 30 m, 40 m, and 50 m, is computed by applying the power-law equation. After that, the wind profile at each height is comparatively estimated based on the graphical, empirical, and maximum-likelihood method. Several statistical tools, namely, root mean square (RMSE) and R-squared (R2), are performed to make a fair comparison among the methods that help estimate the Weibull parameters. The results of this study reveal that the empirical and maximum-likelihood methods exhibit better performance than the graphical method in estimating the shape and scale parameter, irrespective of heights. Moreover, the findings of the study imply that the airport has a moderate potential to make use of generating electricity and hydrogen from wind power.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Estimation of monthly evaporation values using gradient boosting machines and mode decomposition techniques in the Southeast Anatolia Project (GAP) area in Turkey
Sarıgöl, Metin; Katipoğlu, Okan Mert
Today, the biggest issue appears to be the increase in drought in some regions brought on by global warming, which has greatly increased the significance of water management. In light of evaporation's effect on drought, this research intends to evaluate the effectiveness of hybrid machine learning (ML) models, such as the Gradient Boosting Machines (GBM) technique paired with Empirical Mode Decomposition (EMD), Robust Empirical Mode Decomposition (REMD), Ensemble Empirical Mode Decomposition (EEMD), and Variational Mode Decomposition (VMD) signal decomposition techniques, for monthly evaporation prediction models in the Southeast Anatolia Project Area. In the design of the models, 80% of the data was used for training and 20% for testing. Furthermore, tenfold cross-validation was applied to solve the overfitting problem, which negatively affected the forecast performance. In the model setup, various combinations of precipitation, average air temperature, minimum air temperature, maximum air temperature, wind speed, actual air pressure, relative humidity, and solar time variables are presented to artificial intelligence models as input. The study revealed that the GBM methodology in combination with the signal decomposition methods REMD, EMD, EEMD, and VMD generally allowed for more accurate evaporation estimations than the GBM model alone. The study’s results are essential in relation to agricultural production, irrigation planning, water resources management studies, and hydrological modeling studies in the region.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition
Ladouali, Sabrina; Katipoğlu, Okan Mert; Bahrami, Mehdi; Kartal, Veysi; Sakaa, Bachir; Elshaboury, Nehal; Keblouti, Mehdi; Chaffai, Hicham; Ali, Salem; Pande, Chaitanya B.
Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M'sila and M'doukel. Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. The first 6 and 12-month SPI values 1, 2, and 3-month lead time values were estimated with the ELM algorithm. After that, meteorological variables and Standard Precipitation Index (SPI) values, divided into subcomponents with VMD, are presented to the ELM model, and a drought forecasting model is developed. Model performances were evaluated according to various visual and statistical criteria. New hydrological insights for the region: Soft computing techniques have become the preferred method for producing predictions due to their ability to minimize development time, require minimal input, and offer a relatively less complex approach when compared to dynamic or physical models. As a result of the analysis, it has been determined that the highest prediction accuracies are generally obtained in VMD-ELM models and SPI predictions with a 1-month lead time. The study outputs give important ideas to mite donors regarding water resource planning and climate change adaptation strategies in the study area and can be applied to other arid and semi-arid environments.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches
Kartal, Veysi; Katipoğlu, Okan Mert; Karakoyun, Erkan; Simsek, Oguz; Yavuz, Veysel Süleyman; Ariman, Sema
This study aims to predict groundwater drought-based meteorological drought index using machine learning instead of traditional approaches. Groundwater drought (GWD) was predicted using machine learning methodologies such as Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Least Squares Boosting Tree (LSBT), Generalized Linear Regression (GLR) and k-Nearest Neighbours (KNN). In addition, monthly, seasonal, and annual drought indices such as the Standardised Precipitation-Evapotranspiration Index (SPEI), China Z Index (CZI), Standardised Precipitation Index (SPI), Z-Score Index (ZSI), Decile Index (DI), Percent of Normal Index (PNI) and Rainfall Anomaly Index (RAI) were used to analyse the drought of groundwater. These traditional drought indices were modified for the assessment of groundwater drought. Moreover, groundwater drought was predicted based on the hydro-meteorological parameters (temperature, relative humidity, wind speed, rainfall, groundwater level). The applied models’ performances were evaluated via Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), R-squared (R2), Mean Bias Error (MBE), Bias Factor, and Variance Account Factor (VAF). Linear SVM is generally the best model for predicting GWD, while GLR is the second-best performing model. The KNN algorithm obtained the weakest performances. Although all types of drought and wet categories were observed, normal drought occurred more than in the other drought and wet categories. This study can contribute to the assessment of groundwater drought in regions where there is no groundwater station.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Exploring hydrological and meteorological drought trends in Northeast Algeria: implications for water resource management
Berhail, Sabri; Katipoğlu, Okan Mert
This study explores hydrological and meteorological drought in the Coastal Constantine basin, the Cheffia and Mexa dams in northeast Algeria in particular, using a time series of monthly rainfall and dam inflow data over 54 years (1965–2018). Recently, these dams have experienced recurring droughts, significantly reducing water storage and severely impacting the population’s drinking water supply. To better assess the severity and trends of these droughts, we applied a range of methods (Mann-Kendall test, Sen’s slope estimator, Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Innovative Trend Analysis (ITA) method). SPI and SSI were chosen because they can be applied across various timescales, easy to calculate, and have demonstrated accuracy. Additionally, the trend analysis techniques were preferred because they have few assumptions, are resistant to incomplete data, offer the advantage of visual interpretation, and have the ability to capture changes within data subgroups. The findings analysis shows an autumn rainfall increase (1.18 mm/year) and a significant spring decrease (-2.23 mm/year). In addition, an annual rainfall trend magnitude ranging from − 5.26 mm/year to 1.71 mm/year. The Cheffia Dam experiences a summer inflow decrease (-0.05 hm³/year) and an overall annual decline (-0.35 hm³/year), while the Mexa Dam shows a significant spring inflow decrease (-0.17 hm³/year) with a slight annual decline (-0.07 hm³/year). The ITA method revealed severe and prolonged droughts with increasing severity over time, as shown by the SPI and SSI drought trend analysis. These trends affirm the growing impact of reduced precipitation on water resources and emphasize the urgent need for proactive water management strategies in the region. The results highlight the extremely difficult conditions and the severe drought, particularly in central parts of the basin, over the past 54 years, and underscore ongoing challenges in managing water resources amid changing climatic conditions. This situation negatively impacts the Boulatane dam project and the under-construction Boukhroufa dam. Thus, it is necessary to implement necessary measures to mitigate the effects of prolonged droughts on water availability and infrastructure development. The study’s findings provide a foundation for spatial and temporal analysis and management of droughts in water-stressed regions globally, in the context of climate change with innovative techniques. The results emphasize the need for policymakers to prepare for droughts and adopt early warning systems. It is recommended that water conservation efforts be strengthened, new water structures be built, and climate change policies be adapted.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria
Katipoğlu, Okan Mert; Keblouti, Mehdi
The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Evaluating the Accuracy of Machine Learning, Deep Learning and Hybrid Algorithms for Flood Routing Calculations
Sarıgöl, Metin
The increase in average temperatures in the last century has caused global warming, which has increased the frequency of natural disasters. Floods are one of the most important natural disasters and harm the environment and especially human life. Flood routing techniques also play an important role in predicting floods. For this reason, the accuracy and precision of flood routing calculations are of vital importance in taking all necessary precautions before the floods reach the region and in preventing loss of life. This study aims to compare the performance of machine learning, deep learning and hybrid algorithms for flood routing prediction models in the Büyük Menderes River. In this research deep learning model Long-Short Term Memory (LSTM), machine learning model Artificial Neural Network (ANN), and hybrid machine learning models empirical mode decomposition (EMD)-ANN, and particle swarm optimization (PSO)-ANN algorithms were compared to forecast the flood routing results in two discharge observation stations in the Büyük Menderes river. The analysis results of the established ML algorithms were compared with statistical criteria such as mean error, mean absolute error, root mean square error and coefficient of determination. Additionally, Taylor diagrams, box plots, and beeswarm plot visual graphs were also used in this comparison analysis. At the end of the research, it was determined that the hybrid algorithm PSO-ANN was the most successful algorithm in forecasting flood routing results among other models according to the error values of MAE: 0.2514, MSE: 0.4613, RMSE: 0.6791, NSE: 0.941 and MBE: 0.047. Moreover, the LSTM algorithm was the approach with second estimation accuracy. The findings are vital in terms of taking necessary precautions and gaining time before floods reach any region.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies
Sarıgöl, Metin; Katipoğlu, Okan Mert; Dalkilic, Hüseyin Yildirim
Modeling monthly stream flows most accurately is of vital importance for water resource management, agricultural irrigation and efficient hydroelectric energy production, especially in semi-arid areas. Soft computing approaches have recently taken an important place in estimating streamflow time series. The potential of various data-driven approaches to predict streamflow in challenging climate conditions was evaluated. The study used machine and deep learning algorithms to model average monthly stream flows in two stream gauging stations in semi-arid region of the Konya closed basin where agriculture is at the forefront, accurate and reliable estimation of the stream flows is the basis of the study. For this, the performances of emotional neural network algorithm (EmNN), long-short term memory (LSTM), Elman neural network (ENN), nonlinear autoregressive exogenous model (NARX), recurrent neural network (RNN), group method of data handling (GMDH) were compared. The study’s unique contribution lies in its comprehensive comparison of these diverse algorithms, including newer approaches like EmNN, in the specific context of semi-arid hydrology. Partial autocorrelation analysis was applied to select input combinations, and lagged values exceeding 95% confidence limits were presented to the models as the most essential features. Artificial intelligence (AI) models use lagged stream flows to predict the streamflow time series. Statistical parameters, scatter diagrams and a time series approach are used to compare model performance. The GMDH model produced the following test results for 1604 no station: KGE: 0.656, R2: 0.608, NSE: 0.343, RMSE: 27.021, MAE: 3.834, MAPE: 0.662, MBE: −0.217, BF: 0.972. Similarly, for 1623 no station, the GMDH model yielded the following test results: KGE: 0.770, R2: 0.615, NSE: 0.531, RMSE: 0.006, MAE: 0.047, MAPE: 0.217, MBE: −0.012, BF: 0.956. In addition, the EmNN algorithm was the approach with second prediction accuracy. The findings of the study are important resources for optimizing the selection of AI models for streamflow prediction in semi-regional areas. The study also provides critical information for policymakers and decision-makers in similar climate zones worldwide for water resource management.
RDL Kalıcı Kaydıİncele - CorrectionAçık Erişim2024Correction to: Impacts of climate change on spatial drought distribution in the Mediterranean Basin (Turkey): different climate models and downscaling methods (Theoretical and Applied Climatology, (2024), 155, 5, (4065-4087), 10.1007/s00704-024-04867-0)
Erkol, Z. Ibrahim; Yesilyurt, S. Nur; Dalkilic, H. Yildirim
In this article, the affiliation details for author Z. Ibrahim Erkol was incorrectly given as ‘Environmental Sciences Institute, Boğaziçi University, Istanbul, Turkey’ but should have been ‘Brandenburg Technical University Cottbus-Senftenberg, Environmental Engineering Department, Cottbus, Germany’. As for H. Yildirim Dalkilic, it was incorrectly given as ‘Department of Civil Engineering, Dokuz Eylül University, Izmir, Turkey’ but should have been ‘Erzincan Binali Yıldırım University, Faculty of Engineering and Architecture, Department of Civil Engineering, Erzincan, Turkey’. The original article has been corrected.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm
Elbeltagi, Ahmed; Heddam, Salim; Katipoğlu, Okan Mert; Alsumaiei, Abdullah A.; Al-Mukhtar, Mustafa
Study region: Chengdu, Wuhan, Chongqing, and Kunming regions in China. Study focus: Accurate estimation of crop water use or actual evapotranspiration (AET) remains a key obstacle in the effective design of irrigation schedules, plans, and design. This is due to the non-linear nature of this phenomenon. To address this issue and guarantee more accurate ET predictions, this study attempts the following: i) to assess the performance of five machine learning (ML) models optimized by the RReliefF algorithm in estimating actual ET values for each month in four Chinese provinces under various agroclimatic conditions; and ii) to select the optimal model based on statistical metrics while minimizing discrepancies between the estimated and actual ET values. AET was estimated using support vector machine (SVM), ensemble bagged and boosted trees, robust linear regression (RLR), and Matern 5/2 Gaussian process regression (M-GPR) models. New hydrological insights for the region: The M-GPR model outperformed the other models and generated the best values for all statistical measures for training and testing stages: R2 (0.979, 0.982), RMSE (5.56, 5.09), MAE (3.29,3.16). In comparison, the RLR model exhibited the lowest training and testing performances metrics. The results of this study demonstrate the capacity of the M-GPR model to accurately predict long-term AET values. This model is best suited for further research on AET prediction at the stations under investigation, which could improve irrigation and boost agricultural productivity.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Adaptive strategies and thermal perceptions in intermittently used congregational Spaces: A case study
Gemici, Zafer; Dogan, Ahmet
In spaces with considerable volumes that are sporadically used and typically not operating at full capacity, existing thermal comfort models face limitations. This research aims to reveal disparities between these models and survey-based comfort assessments. Comfort calculations were conducted using conventional thermal comfort models based on measured parameters and compared with survey data. Notable differences prompted the formulation of a more robust correlation, accounting for 83.4 % of the variations in Predicted Mean Votes (PMV). A regression model derived from survey data shows a high coefficient of determination (R2 = 0.84), supporting its applicability for large intermittently used spaces like mosques. Addressing limitations of existing models, this research introduces a practical and simplified approach to predicting thermal comfort, enhancing assessments in these distinctive spaces. The study also examines mosque indoor air quality dynamics. Despite sporadic ventilation, CO2 levels rose from 1500 ppm to 2500 ppm during Friday prayers, posing potential health risks. Before prayers, indoor CO2 and humidity met ASHRAE Standard 62, ISO 7730, and EN15231 values. To mitigate health concerns from elevated CO2 levels, improved ventilation systems are necessary. Comfort temperatures were only achieved during prayer periods.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting
Katipoğlu, Okan Mert; Ertugay, Neşe; Elshaboury, Nehal; Aktürk, Gaye; Kartal, Veysi; Pande, Chaitanya Baliram
Drought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the Firefly Algorithm (FFA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long short-term memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468–0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443–0.916) at station 1612 generally have the highest accuracy.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Trends of changing land use dynamics in the Terkos Lake basin between 1980 and 2023 and their impact on natural ecosystems
Şenol, Celal; Taş, Mehmet Akif
Anthropogenic processes are one of the main causes of environmental change in this century. As an indicator of man’s struggle for dominance over nature, the environment has been directly affected. These changes, which we encounter in every region of the place where we live, on a local and global scale, and which are due to man, cause the natural cycle of ecosystems to be disrupted. As in Türkiye in general, land use changes in the study area under the influence of direct and indirect factors in some regions are very rapid. No matter how much effort are made to control them, factors that have a direct impact on the environment, such as population growth, agriculture, industrial facilities, and the design of transport infrastructure, are the most important causes of change. In this direction, this study aims to identify the trends of land use changes around Lake Terkos and to reveal the deficiencies and fragilities of the relationship networks. It is also an indication of possible changes in the ecological status of the lake and its surroundings in relation to this situation. Terkos Lake’s prolonged use as a crucial drinking water source for Istanbul makes it a valuable study site to investigate its ecological state and management practices. Nevertheless, it has recently been under pressure due to transportation (airport, Northern Marmara Motorway, Istanbul Canal and other transport networks), urban sprawl, and industry. In this context, first of all, the dynamics of land use and the rapidly changing areas (hot zones) between 1980 and 2023 have been identified and the extent of the changes over time has been shown. Possible future land use changes were analysed in the light of this data. As a result of the study, the speed of transformation of green areas was revealed. In this direction, especially in the field of the aquatic ecosystem, vulnerable areas were identified, the degree of being affected by future change was revealed and the trend in the dimensions of use and change was analysed.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023A detailed emission analysis between regional jet and narrow-body passenger aircraft
Kilic, Ugur
In this study, a comparison in terms of environmental impact is carried out between regional jet and narrow-body passenger aircraft with different Maximum Take-Off Weights (MTOW) and seating capacities considering the fuel burn and emissions. The flight trajectory is selected from Erzincan Yıldırım Akbulut Airport to Ankara Esenboğa Airport and that trajectory is a frequently performed domestic flight between Erzincan and Ankara. Integrated Aircraft Noise and Emissions Modelling Platform (IMPACT) developed by EUROCONTROL is used for the calculation of fuel burn, CO2, H2O, and other gas emissions (NOx, SOx, CO, HC, soot, and other trace compounds) for the per phase of flight. These emissions have an impact on human health, air quality, and the ecosystem, and cause air pollution, climate change, and global warming worldwide. Commercial air-transport-based simulations are created for regional jet and narrow-body passenger aircraft. According to the results, flying with regional jets which have lower MTOW from/to airports such as Erzincan Yıldırım Akbulut Airport, where the passenger density per aircraft is low, provides advantages in terms of fuel burn and emissions. It is expected that this study will serve as a guide for airline operators for fleet selection based on fuel burn and emission parameters.
RDL Kalıcı Kaydıİncele
