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A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting
(Pergamon-Elsevier Science Ltd, 2024) Katipoglu, Okan Mert; Ertugay, Nese; Elshaboury, Nehal; Akturk, 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 shortterm 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.
A novel semi data dimension reduction type weighting scheme of the multi-model ensemble for accurate assessment of twenty-first century drought
(Springer, 2024) Mukhtar, Alina; Ali, Zulfiqar; Nazeer, Amna; Dhahbi, Sami; Kartal, Veysi; Deebani, Wejdan
Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann-Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management.
Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition
(Springer Heidelberg, 2024) Elbeltagi, Ahmed; Katipoglu, Okan Mert; Kartal, Veysi; Danandeh Mehr, Ali; Berhail, Sabri; Elsadek, Elsayed Ahmed
Various critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ETo). In this context, our study aimed to enhance the accuracy of ETo prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)-artificial neural network (ANN) (codename: ABC-ANN). To this end, historical (1979-2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ETo prediction. Our results showed that the highest ETo prediction accuracy was obtained with ABC-ANN (Train R-2: 0.990 and Test R-2: 0.989), (Train R-2: 0.986 and Test R-2: 0.986), (Train R-2: 0.991 and Test R-2: 0.989) and (Train R-2: 0.988 and Test R-2: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ETo prediction.
Assessing the generalization of forecasting ability of machine learning and probabilistic models for complex climate characteristics
(Springer, 2024) Batool, Aamina; Ali, Zulfiqar; Mohsin, Muhammad; Masmoudi, Atef; Kartal, Veysi; Satti, Samina
Climate changes and global warming increase risk of recurrent extreme and complex climatic features. It necessitates accurate modeling and forecasting of climate phenomena for sustainable development goals. However, machine learning algorithms and advanced statistical models are extensively employed to analyze complex data and make predictions related to climate phenomena. It is important to have comprehensive knowledge to use these models and consider their potential implications. This study aims to evaluate and compare some popular machine learning and probabilistic methods by analyzing various time series indices associated with precipitation and temperature. For application, time series data of Standardized Precipitation Temperature Index (SPTI), Standardized Temperature Index (STI), Standardized Compound Drought and Heat Index (SCDHI), and Biased Diminished Weighted Regional Drought Index (BDWRDI) are used from various meteorological regions of Pakistan. The performance of each algorithm is compared using Residual Mean Square Error (RMSE) and Mean Average Error (MAE). The outcomes associated with this research indicate a higher preference of neural networks over machine learning methods in the training sets. However, the efficiency varies from model to model, indicator to indicator, time scale to time scale, and location to location during the testing phase. The most appropriate models are found by considering a list of candidates forecasting models and investigating the performance of each model.
Assessment of drought using different tests and drought indices in Elazig, Turkey
(Iwa Publishing, 2023) Kartal, Veysi
Water is one of the most essential elements for human life and must be provided for life requirements. Historical changes in meteorological data are vital for the planning and operation of water management. A total of 516-time series were used to evaluate the characteristics of drought in Elazig in Turkey. In this study, meteorological drought analysis was carried out in monthly and annual periods by using the standardized precipitation evapotranspiration index (SPEI), standardized precipitation index (SPI, innovative polygon trend analysis (IPTA), and China-Z Index (CZI) drought indices. As a result, it was determined that there was an increase in dry periods for all time scales for eight meteorological stations, especially in 2,000 and after. A downward trend was detected in precipitation data, while an upward trend was detected in temperature and evaporation data based on a 95% confidence interval. Although normal drought has the highest share among drought categories, very severe drought has the lowest share. It is determined that SPI gives more sensitive results in the very severe drought category than the SPEI index. As a result, the region's trend of rain and temperature will assist water management for resource planning.
Assessment of meteorological, hydrological and groundwater drought in the Konya closed basin, Türkiye
(Springer, 2024) Kartal, Veysi; Nones, Michael
Water scarcity, and drought in particular, is a major challenge worldwide, causing direct and indirect negative effects on ecological systems and water resources, as well as social and economic aspects of life. Climate change and increasing human pressure are contributing to increasing the likelihood of droughts, impacting regions which were not used to be dry. To address this challenge properly, studies should be performed at a multi-scale level, addressing hydrological and hydrogeological drought. Focusing on the Konya Closed Basin in T & uuml;rkiye, data derived from nineteen stations were used to analyze drought conditions, looking at multiple meteorological-Standardized Precipitation Index (SPI), Z Score Index (ZSI), China Z Index (CZI), Modified China Z Index (MCZI)-hydrological-Streamflow Drought Index (SDI), Surface Water Supply Index (SWSI)-and hydrogeological-Standardized Groundwater Level Index (SGI)-assessment indices for different time scales (1, 3, 6,12, 24, 36 and 48 months). The results show that extreme drought (ED) conditions computed by SPI at 1 month (1.9-2.5%) were higher than that of all indices for all stations. Moderate drought occurred at least according to the ZSI-1. It was determined that the percentage of SPI and CZI had greater Extremely, Severely and Moderately Dry events (10.7-13.4% for CZI; 10.7-14.2% for SPI) than those of ZSI and MCZI. On the other part, MCZI has shown fewer total drought events (6.2-10%). Since 2008, extremely dry conditions in the Konya Closed Basin are generally caused by groundwater drought, which is higher than meteorological and hydrological droughts. The results reported in this work might help in better planning drought-resilient strategies in the basin, which will be paramount in light of climate change.
Climate change trends in the Southeastern Anatolia region of Turkiye: precipitation and drought
(Iwa Publishing, 2024) Kartal, Veysi; Yavuz, Veysel Suleyman; Ariman, Sema; Kaya, Kubra; Alkanjo, Safa; Simsek, Oguz
Drought, earthquake, flood, and fire are disasters whose effects occur after a more extended period than other disasters. Meteorological drought is called the beginning of drought types. In this study, trend analyses and temporal changes in temperature, precipitation, and drought index values were carried out between 1981 and 2022 at three meteorological observation stations in the Southeastern Anatolia Region of Turkiye. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Chinese Z Index, and Effective Drought Index methods were used for drought analysis, while Sen's slope, Mann-Kendall, and innovative trend analysis methods were used to detect the trend of precipitation. It was determined that precipitation generally tended to decrease, and drought increased since 1996. Although every type of dry and wet periods has occurred, normal dry periods were observed more. In the spatial distribution of drought, the inverse distance weighted method gives larger areas with more extreme drought and wet values than the Kriging method. The increase in extreme values in the region indicates that the severity of drought will increase. It has been determined that the region's water resources and agricultural activities are under pressure due to climate change and drought.
Comparative Analysis of Water Quality in Major Rivers of Türkiye Using Hydrochemical and Pollution Indices
(Mdpi, 2024) Yavuz, Veysel Sueleyman; Kartal, Veysi; Sambito, Mariacrocetta
This study provides a comprehensive analysis of the water quality in five major rivers in T & uuml;rkiye: Sakarya, Ye & scedil;il & imath;rmak, K & imath;z & imath;l & imath;rmak, Seyhan Rivers, and Ni & gbreve;de Creek. Utilizing hydrochemical diagrams and the River Pollution Index (RPI), we assess the temporal and spatial variations in water quality over the past three decades. The hydrochemical characteristics reveal a dominant calcium-bicarbonate (Ca-HCO3) type water, indicating strong geological control primarily influenced by carbonate weathering. Seasonal variations and anthropogenic influences, particularly agricultural runoff and industrial discharge, contribute to significant changes in ion concentrations, especially in the K & imath;z & imath;l & imath;rmak and Ye & scedil;il & imath;rmak Rivers. The RPI results classify these rivers as 'Severely polluted' to 'Moderately polluted', driven by high levels of suspended solids and biochemical oxygen demand. This study underscores the urgent need for tailored pollution control measures and sustainable water management practices in order to mitigate the impacts of anthropogenic activities and protect the ecological health of these vital water resources. The findings provide a robust framework for future research and policymaking to enhance water quality monitoring and management strategies in the region.
Comparison of different techniques in determining groundwater levels trends in Türkiye
(Wiley, 2024) Kartal, Veysi; Nones, Michael; Topcu, Emre; Ariman, Sema
Groundwater represents one of the largest resources of freshwater in the world. Thus, investigations of groundwater level variations due to climate change and increasing human activities are of great importance, especially in resource scarce regions. Our research aimed to understand the long-term effects of climate events and water use on groundwater levels over the study area via Mann-Kendall, Sen's Slope, Innovative Polygon Trend Analysis (IPTA), and Innovative Trend Analysis (ITA) analyses. Although several studies are available in relation to GWL trend analysis via ITA, Mann-Kendall and Sen slope in the literature, there are few IPTA studies conducted. The focus of the study was seven wells across T & uuml;rkiye over the period 1987-2022. Results demonstrate that there was a downward trend in GWL in all stations annually, regardless of the method. At monthly scale, a decrease was noted, especially in June, August, and September, while seasonally, decreases were seen in autumn and winter. Moreover, it was evident the results of the Sen slope and ITA were compatible, while the IPTA was a useful tool in detecting GWL trends. Identifying and understanding GWL trends is highly valuable in informing groundwater resource managers of critical areas of overuse and other factors affecting groundwater, resulting in preventive interventions to overcome such problems and protect this critical resource. GWL trends are analysed at the monthly, seasonal, and annual scales using a combination of techniques, such as Mann-Kendall, Sen's Slope, Innovative Polygon Trend Analysis (IPTA), and Innovative Trend Analysis (ITA), using data monitored between 1987 and 2022 in seven wells in Turkey. Trend analysis will allow for evaluating differences among commonly used methods in investigating GWL levels, eventually providing insights on critical areas to prioritize interventions. Besides analysing trends, a homogeneity analysis was conducted to detect change points in groundwater levels. image
Development of Divergence and Interdependence-based Hybrid Weighting Scheme (DIHWS) for accurate assessment of regional drought
(Springer Wien, 2024) Mukhtar, Alina; Ali, Zulfiqar; Kartal, Veysi; Karakoyun, Erkan; Yousaf, Mahrukh; Sammen, Saad Sh.
Accurate ensembles of precipitation data play an important role in precise and efficient drought monitoring systems at the regional level. This article proposes a weighted aggregation scheme - the Divergence and Interdependence-based Hybrid Weighting Scheme (DIHWS) - to ensemble precipitation data for accurate regional drought analysis. The derivation of weights is based on the interdependence among meteorological observatories and the divergence from the mean characteristics of regional data. Here, the interdependence among meteorological observatories is assessed using the Bayesian Network theory. At the same time, the divergence from the mean characteristics of regional data is based on the set of equations used for regional aggregation in (Ali et al., Water Resour Manage 36:4099-4114, 2022). Consequently, the paper introduces a new regional drought index - the Bayesian Network-based Adaptive Regional Drought Index (BNARDI). BNARDI is a standardized regional index and used estimated at multiple time scales. The application of DIHWS and BNARDI is based on five regions of varying observatories. We observed smaller MAE values associated with DIHWS than its Simple Model Average (SMA) and one other of its relevant compitator in all the regions. Therefore, we conclude that the proposed weighting scheme and drought index are more reliable for regional drought monitoring and forecasting. Additionally, the research includes various forecasting models to assess their appropriateness for forecasting the new regional index. The results of this research demonstrate that no single method is suitable for forecasting complex drought data, as generated by BNARDI. Therefore, we suggest using varying methods or a hybrid of various candidate forecasting models for forecasting BNARDI.
Drought Assessment of Yeşilırmak Basin Using Long-term Data
(2024) Kartal, Veysi
Drought is a prolonged period of inadequate rainfall, such as one season, one year or several years, on a statistical multi-year average for a region. Drought is a natural disaster effective on several socio-economic activities from agriculture to public health and leads to deterioration of the environment sustainability. The drought starts with meteorological drought, continues with agricultural and hydrological drought, and when it is in the socioeconomic dimension, the effects begin to be observed. Generally, drought studies are based on drought indices in the literature. This study applied long-term precipitation, temperature, and evaporation data from Samsun, Tokat, Merzifon, Çorum and Amasya meteorological stations from 1961 to 2022 to investigate the drought in the Yeşilırmak basin of Turkey. The present study applied Standardized Precipitation Index (SPI), and Effective Drought Index (EDI), China Z- Index (CZI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on daily, monthly, seasonal, and annual time periods to evaluate drought. The Sen slope and Mann-Kendall test were employed for data analysis. The results revealed that the monthly drought indices for the study area were almost identical for the study area. Although dry and wet periods were observed.
ELAZIĞ’IN METEOROLOJİK VE TARIMSAL KURAKLIĞININ FARKLI KURAKLIK İNDİSLERİ KULLANILARAK DEĞERLENDİRİLMESİ
(2024) Kartal, Veysi
Su insan hayatı için elzem olduğu gibi yaşayan tüm canlılar için de bir ihtiyaçtır. Dolayısıyla, suyun varlığı yaşamın devamı için gereklidir. Bu bağlamda suyun varlığını ya da eksikliğini yani kuraklığı incelemek için kuraklık indisleri yaygın olarak kullanılmaktadır. Sonuç olarak, nedeni veya etkisi ne olursa olsun sürekli değişen iklim, su açığının sınırını zorlamaktadır. Elazığ gölleri, akarsuları ve yeraltı suları olmak üzere çeşitli su kaynaklarına sahip ve Türkiye'nin tarımında (kayısı meyvesi üretimi) önemli bir şehirdir. Bu çalışmada, Türkiye'nin Doğu bölgesinde yer alan Elazığ’ın kuraklığını araştırmak için 1979-2022 yılları arasında 8 meteoroloji istasyonundan elde edilen uzun vadeli yağış, sıcaklık ve buharlaşma kayıtları kullanılmıştır. Mevcut çalışmada, kuraklığı değerlendirmek için aylık ve yıllık zaman periyodları kullanılarak meteorolojik kuraklık için Standartlaştırılmış Yağış İndeksi (SPI) ve Keşif Kuraklık İndeksi (RDI), tarımsal kuraklık için ise Etkili Keşif Kuraklık İndeksi (eRDI) kullanılmıştır. Veri analizi için Sen eğimi ve Petttitt testi kullanılmıştır. Çalışma alanı boyunca kuraklık indis sonuçlarının neredeyse aynı olduğu tespit edilmiştir. Özellikle Ağın ve Baskil ilçelerinin kuraklıkla karşı karşıya olduğu tespit edilmiştir. Mevcut çalışmada, meteorolojik ve tarımsal kuraklık için şiddetli kuraklık dönemler olmasına rağmen, genel olarak normal kuraklık seviyeleri gözlemlenmiştir. Ancak yine de bazı istasyonlarda aşırı kurak veya aşırı yağışlı dönemler de gözlemlenmiştir.
Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation
(Springer Heidelberg, 2024) Akiner, Muhammed Ernur; Kartal, Veysi; Guzeler, Anil Can; Karakoyun, Erkan
The Ye & scedil;il & imath;rmak River Basin in northern T & uuml;rkiye is crucial for the region's water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Ye & scedil;il & imath;rmak River basin (R2 = 0.98, RMSE = 0.18). The study's findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.
Hydrological Drought and Trend Analysis in Kızılırmak, Yeşilırmak and Sakarya Basins
(Springer Basel Ag, 2024) Kartal, Veysi; Emiroglu, Muhammet Emin
Water is one of the most essential elements for human life and must be provided for life necessities. Historical changes in hydro-meteorological data are vital for operating and planning water structures. Drought indices are commonly used in the literature to assess the drought. Long-term streamflow records were used to evaluate the hydrological drought based on the Stream Flow Drought Index (SDI) in Sakarya, K & imath;z & imath;l & imath;rmak and Ye & scedil;il & imath;rmak basins located in Turkey for 56 years (1965-2020) with sixteen stations. SDI values were calculated at 1-, 3-, 6-, and 12-month scales based on moving averages (MA) to analyze the drought. Run Test and Double Mass Curve were applied to analyze the streamflow data. Moreover, Sen's Innovative Trend Detection Test (SITDT), Innovative Polygon Trend Analysis (IPTA), Mann-Kendall and Sen's slope were applied to evaluate the trend in the streamflow. Findings show that similar results were obtained for SDI-1, SDI-3, SDI-6, and SDI-12 results based on MA at the same stations. Although different droughts occurred, normal droughts were observed more. Downward trends were detected in streamflow data based on IPTA, SITDT, Mann-Kendall and Sen's slope. As a result, assessment of hydrological drought and trend analysis in these basins will contribute to water resources planning and management in the basins.
Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria
(Mdpi, 2023) Achite, Mohammed; Katipoglu, Okan Mert; Jehanzaib, Muhammad; Elshaboury, Nehal; Kartal, Veysi; Ali, Shoaib
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM's fast-learning ability and discrete wavelet transform's ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.
Machine learning-based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change
(Wiley, 2024) Kartal, Veysi
Water is essential for humans as well as for all living organisms to sustain their lives. Therefore, any climate-driven change in available resources has significant impacts on the environment and life. Global climate models (GCMs) are one of the most practical methods to evaluate climate change. Based on this, this research evaluated the capability of GCMs from the Coupled Model Intercomparison Project 6 (CMIP6) to reproduce the historical flow of climate prediction centre data for the Konya Closed basin and to project the climate of the basin using the selected GCMs. Global climate models based on the CMIP6 under the scenario of common socioeconomic pathways (SSP245 and SSP 585) were used to analyse the climate change effect on streamflow of the study area by Bias Correction of GCM Models using Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), AdaBoost, Gradient Boosting, Regression Tree, and Random Forest methods. The coefficient of determination (R-2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) were used to assess the performance of the methods. Findings show that the Random Forest Model consistently outperformed other models in both the testing and training phases. A significant downward in the volume of water flowing through the region's rivers and streams in the next decades. It is critical to enhance climate-resilient water infrastructure financing, establish an early warning system for drought, introduce best management practices, implement integrated water resource management, public awareness, and support water research to alleviate the negative consequences of drought and increase resilience against the effects of climate change on Turkey's water resources.
Modeling of irrigation water quality parameter (sodium adsorption ratio) using hybrid swarm intelligence-based neural networks in a semi-arid environment at SMBA dam, Algeria
(Springer Wien, 2024) Achite, Mohammed; Katipoglu, Okan Mert; Elshaboury, Nehal; Kartal, Veysi; Akturk, Gaye; Ertugay, Nese
Sodium adsorption rate (SAR), which significantly affects soil and plant health, is determined according to the concentration of sodium ions, calcium, and magnesium in irrigation water. Accurate estimation of SAR values is vital for agricultural production and irrigation. In this study, hybrid swarm intelligence-based neural networks are used to model sodium adsorption ratio in irrigation water quality parameters in a semi-arid environment at Sidi M'Hamed Ben Aouda (SMBA) dam, Algeria. For this, the nature-inspired optimization techniques of particle swarm optimization (PSO), genetic algorithm (GA), Jaya algorithm (JA), artificial bee colony (ABC), and firefly algorithm (FFA) and the signal processing technique of variational mode decomposition (VMD) have been combined with artificial neural networks (ANN). Correlation matrices were used to select the data entry structure in the established models. Water quality parameters with a statistically significant and medium to high relationship with SAR values were presented as input to the model. The overall performance was measured using various statistical metrics, scatter diagrams, Taylor diagrams, correlograms, boxplots, and line plots. In addition, the effect of input parameters on model estimation was evaluated according to Sobol sensitivity analysis. As a result, the GA-ANN algorithm demonstrated superior performance (MSE = 0.073, MAE = 0.193, MAPE = 0.048, MBE=-0.16, R2 = 0.934, WI = 0.968, and KGE = 0.866) based on the statistical indicators, indicating better results compared to other models. The second-best model, ABC-ANN (MSE = 0.084, MAE = 0.233, MAPE = 0.066, MBE=-0.135, R2 = 0.897, WI = 0.965, and KGE = 0.920) was also selected. The weakest prediction outputs were obtained from the VMD-ANN model. The accurate and reliable estimation of SAR in irrigation water has the potential to facilitate improvements in agricultural irrigation management and agricultural production efficiency for farmers, agricultural practitioners, and policymakers.
Numerical simulation of the flow passing through the side weir-gate
(Elsevier Ltd, 2024) Kartal, Veysi; Emin Emiroglu, M.
A combination of a weir and a gate is called the combined weir-gate. The hydraulic behaviour of the combined side weir-gate was numerically and experimentally investigated in the study. The findings were evaluated to investigate the impact of combined flow and weir-gate geometry characteristics on the discharge capacity of the side weir-gate. Thirty-three numerical simulations were conducted. The numerical simulations were performed by applying the Volume of Fluid (VOF) method to the Reynolds-averaged Navier–Stokes (RANS) equations using Flow-3D using k-? turbulence model. The model was validated against experimental data previously obtained by the authors, and eighty-eight experiments were conducted to analyze using different models. The findings show that the models with rectangular weir and gate cross-sections have higher discharge capacity than those with triangular and semicircular gates, and the models with semicircular gates have higher discharge capacity than those with triangular gates. The Absolute Percentage Error (APE), Root Mean Square Error (RMSE), Scatter Index (SI) and Coefficient of Determination (R2) of the numerical and experimental results are 4.11 %, 0.30 % and 0.10 %, 0.99, respectively. The results show that experimental and numerical findings and discharge capacity are consistent. © 2024 Elsevier Ltd
Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights
(Springer, 2024) Kartal, Veysi; Karakoyun, Erkan; Akiner, Muhammed Ernur; Katipoglu, 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, K & imath;r & scedil;ehir, and Gemerek stations, respectively. The RF, ANN and GLR models provided second best results for these stations, respectively.
Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches
(Pergamon-Elsevier Science Ltd, 2024) Kartal, Veysi; Katipoglu, Okan Mert; Karakoyun, Erkan; Simsek, Oguz; Yavuz, Veysel Suleyman; 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 kNearest 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), ZScore 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.

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