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ArticleAçık Erişim2024/r/ebyu/12787
Hybrid machine learning models for groundwater level prediction in a snow-dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques
Gezici, Kadir; Katipoğlu, Okan Mert; Şengül, Selim
Water scarcity is a pressing issue, intensified by factors such as population growth and industrialization. Hence, it is crucial to monitor, conserve and analyse groundwater resources, which are essential sources of clean and usable water. This study examines changes in groundwater levels (GWLs) in northeastern Turkey's mountainous and snow-covered area. The primary objective is to assess the effectiveness of integrated machine learning models, specifically, the extreme learning machine (ELM) technique combined with signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and empirical wavelet transform (EWT) for monthly GWL prediction models. Seventy percent of the accessible data is allocated for training, while 30% is designated for testing. A correlation matrix involving precipitation, temperature, relative humidity and GWL parameters is generated with inputs that possess significant correlations being selected, such as GWLt−1, GWLt−2, RHt, RHt−1 and RHt−2. To evaluate model results, various metrics, including mean squared error, mean absolute error, mean absolute percentage error, mean bias error, bias factor, determination coefficient, Nash-Sutcliffe efficiency, as well as tools such as box plots, Taylor diagrams and radar charts, are utilized to compare outcomes during the interpretation phase. The results of the analyses show that applying data decomposition methods such as EEMD, VMD and EWT significantly improves the performance of the ELM algorithm in predicting GWLs. VMD-ELM is the most accurate for GWL forecasting among the approaches examined. The R2 values of the most successful models established in the two wells are 0.993 and 0.905. The outcomes of this research hold significance for decision-makers and policymakers as it offers informative insights into aquifer surveillance, irrigation strategizing and efficient administration of water resources.
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CorrectionAçık Erişim2024/r/ebyu/12776
Correction to: Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran (Neural Computing and Applications, (2024), 36, 20, (12609-12624), 10.1007/s00521-024-09681-3)
Mirzania, Ehsan; Achite, Mohammed; Elshaboury, Nehal; Katipoğlu, Okan Mert; Saroughi, Mohsen
In this article Mohsen Saroughi should have been denoted as a second corresponding author. The original article has been corrected.
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ArticleAçık Erişim2024/r/ebyu/12721
Land Cover Dynamics and Environmental Implications in Türkiye’s Riparian Zones: A Comprehensive Seven-Year Study
Akturk, Emre; Guney, Kerim; Aydin, Mirac; Yildiz, Faruk; Kaya, Ozkan
This study examines the Riparian Buffer Zones (RBZs) in Türkiye, analyzing land cover changes from 2017 to 2023 to assess their effectiveness in ecological conservation and water resource management. The research employs Esri-Sentinel land cover data, Shuttle Radar Topography Mission digital elevation models, and RiverATLAS data to identify and delineate RBZs, classify land cover types, and evaluate trends and changes over seven years. The findings indicate that 44.8% of RBZs are covered by ‘Rangelands’, 32.2% by ‘Crops’, and 16.1% by ‘Trees’ classes. The study notes a considerable increase in built areas and agricultural fields, which may indicate ongoing urban expansion and its potential impact on the effectiveness of RBZs. Despite regulatory efforts, the substantial presence of agriculture within RBZs indicates a need for more robust environmental protection policies. The results indicate a pressing need for a policy reassessment to enhance forest cover within RBZs and reduce agricultural encroachment. This would improve water quality and facilitate the achievement of sustainability goals numbered 2, 3, 6, 11, 14 and 15. The findings thus call for a strategic enhancement of RBZ management to better protect water resources against the adverse effects of agricultural and urban activities.
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ArticleAçık Erişim2024/r/ebyu/12701
Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)
Saroughi, Mohsen; Mirzania, Ehsan; Achite, Mohammed; Katipoğlu, Okan Mert; Ehteram, Mohammad
Abstract: Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm’s success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and − 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to − 344, − 332.8, and − 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction. Graphical Abstract: (Figure presented.).
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ArticleYalnız künye2024/r/ebyu/12700
Prediction of monthly groundwater level using a new hybrid intelligent approach in the Tabriz plain, Iran
Mirzania, Ehsan; Achite, Mohammed; Elshaboury, Nehal; Katipoğlu, Okan Mert; Saroughi, Mohsen
Predicting the groundwater level (GWL) is essential in water resource management and irrigation planning in arid and semi-arid areas. In this study, an artificial neural network (ANN) was combined with newly developed wild horse optimizer (WHO) and egret swarm optimization algorithm (ESOA) techniques to predict a one month lead-time GWL in the Tabriz plain of Iran. For the prediction of the GWL, the number of months and years, the one month lag of average temperature, evaporation, precipitation, and GWL were used as inputs. Model performances were compared using root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and relative strength ratio (RSR) statistical indicators and scatter diagrams, time series graph, violin graph, and Taylor diagram. As a result of the analysis, the most successful estimation results were obtained with the input combinations of year, month, average temperature, evaporation, precipitation, and GWL (t − 1) for the prediction of the one month lead-time GWL. According to the results of evaluation indicators in the testing phase, ANN with (R2 = 0.871, RMSE = 0.306 (m), NSE = 0.832, and RSR = 0.410), WHO–ANN (R2 = 0.932, RMSE = 0.200 (m), NSE = 0.929, and RSR = 0.267), and ESOA–ANN (R2 = 0.952, RMSE = 0.164 (m), NSE = 0.951, and RSR = 0.220). In addition, it was revealed that the ESOA–ANN hybrid model showed higher prediction success than the WHO–ANN and standalone ANN models. The study outputs contribute to decision-makers and planners for controlling land subsidence, assessing GWL and aquifer compaction, irrigation planning, and effective management of water resources.
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ArticleAçık Erişim2024/r/ebyu/12684
Analyzing the dynamical relationships between meteorological and hydrological drought of Wadi Mina basin, Algeria using a novel multiscale framework
Achite, Mohammed; Simsek, Oguz; Sankaran, Adarsh; Katipoğlu, Okan Mert; Caloiero, Tommaso
Understanding the relationship between hydrological and meteorological drought in drought-prone regions is critical for proper reservoir management. This study presents a novel multiscale framework for investigating the associations between hydrological and meteorological drought based on the Time-Dependent Intrinsic Correlation (TDIC) method. Firstly, the characteristics of short, medium and long term standardized precipitation index (SPI) and the standardized runoff index (SRI) of the Wadi Mina basin (Algeria) have been analyzed based on data from 6 rainfall and hydrometric stations. Then an Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN) method is used to decompose the most correlated SPI and SRI series to different scales. A stronger association between the two types of droughts is evident in the low-frequency trend component regardless of the station, but their evolution pattern does not remain the same. Subsequently, a TDIC based running correlation analysis is performed between the modes to examine the SPI–SRI associations over the time domain and across the time scales. TDIC analysis has proven the dynamic behavior in the SPI–SRI associations bearing frequent alterations in nature and strength across the process scales and along the time domain. In general, at the intra-annual scales the SPI–SRI correlations are mostly weak positive with localized alterations to negative along the time domain, whereas the relationship is dominantly strong positive and long range at inter-annual scales up to 4 years. This dynamic behavior in the SPI–SRI association and the evolution pattern of trend decipher that the rainfall processes are not directly transferred to streamflow drought, but it also gets controlled by many other local meteorological processes.
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ArticleAçık Erişim2024/r/ebyu/12595
Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran)
Saroughi, Mohsen; Mirzania, Ehsan; Achite, Mohammed; Katipoğlu, Okan Mert; Al-Ansari, Nadhir; Vishwakarma, Dinesh Kumar; Chung, Il-Moon; Alreshidi, Maha Awjan; Yadav, Krishna Kumar
The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to −25.3%, −29.6% and −57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has −204.9 value for AIC which has grown by 5.23% (−194.7) compared to the state without any pre-processing method (ANN_Relu_25).
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ArticleAçık Erişim2024/r/ebyu/12568
Prussian blue modified magnetite decorated MW-CNT nanocomposite: synthesis, characterization and potential use as Photo-Fenton catalysts for phenol degradation
Gögen, Yusuf; Koçak, Çağrı Ceylan; Karabiberoğlu, Şükriye; Polatdemir, Elmas; Aslaner, Harun; Zorlu, Rabia; Çağlar, Bülent; Çirak, Çağrı; Özdokur, Kemal Volkan
Removal of organic pollutants with green and efficient methods is an important goal for researchers studying environmental pollution. Photo-Fenton systems with composite catalysts are a promising alternative for efficient water treatment. In this study, Prussian blue modified magnetite decorated multi-walled carbon nanotubes (MW-CNT) nanocomposite was prepared and for the first time applied to the Photo-Fenton degradation of phenol. The catalyst was prepared by rapid hydrothermal method. XPS, SEM–EDX, XRD, FTIR, UV–Vis–NIR, EIS techniques were used for characterizing the functionalities and morphology of the catalyst. The effect of initial H2O2 concentration, catalyst mass, and initial pH of the phenol solution was investigated. Experimental results showed that 100 mg dm−3 of phenol could be degraded completely in 15 min at pH 7. This study describes the synthesis of the new Photo-Fenton catalyst for highly effective phenol treatment.
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ArticleYalnız künye2024/r/ebyu/12561
Integration 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.
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ArticleYalnız künye2024/r/ebyu/12552
An election algorithm combined with support vector regression for estimating hydrological drought
Achite, Mohammed; Emami, Somayeh; Jehanzaib, Muhammad; Katipoğlu, Okan Mert; Emami, Hojjat
The prediction of hydrological drought is significant for the management of water resources, planning of hydroelectricity production, agricultural production, habitat, and life of living things. The primary aim of this study is to increase the prediction success hydrological drought in the Wadi Ouahrane basin (270 km2). For this purpose, support vector regression technique is hybridized with Election Algorithm. Standardized Runoff Index (SRI) values were used to determine hydrological droughts. In calculating droughts, rainfall and stream flow data covering the years 1972–2018 were employed. Standardized Precipitation Index (SPI) and previous SRI values were used in the establishment of the hydrological drought prediction model. The coefficient of determination, Standard deviation, the Akaike information criterion and root-mean-square error values were used to test the model accuracy. It was identified to be the most accurate according to the research outputs were obtained with SRI-12 and the Election Algorithm-based support vector regression algorithm showed successful results in hydrological drought prediction. In addition, it was revealed that realistic results are obtained when SPI value and delayed SRI values were used as inputs in multi-time scale analysis of hydrological drought.
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ArticleAçık Erişim2024/r/ebyu/12442
Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm
Moazenzadeh, Roozbeh; Katipoğlu, Okan Mert; Shateri, Ahmadreza; Nasiri, Hamid; Abdallah, Mohammed
This study aimed to develop an accurate and reliable model for predicting suspended sediment load (SL) in river systems, which is crucial for water resource management and environmental protection. While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employed in previous studies, the novelty of this research lies in the introduction of a hybrid approach that synergistically combines XGB with the bio-inspired Marine Predators Algorithm (XGB-MPA) to estimate SL in the Yeşilirmak River (Turkey). To this end, streamflow (Q) and sediment concentration (SC) values as well as their lag times (1 to 3 month lag times) were fed as input variables–under 9 scenarios–into ML models. A time series of datasets from March 1973 to December 2011 and January 2012 to March 2023 were used for training and testing of ML models, respectively. The superiority of the proposed model (XGB-MPA) compared to two other hybrid models, including XGB-PSO (Particle Swarm Optimization) and XGB-GWO (Grey Wolf Optimization) was also investigated. According to the results, the simultaneous application of Q and SC lag time values as inputs has led to the best SL estimates by XGB-MPA, with XGB-MPA9 (RMSE = 103.7 ton/day; NSE = 0.96) exhibiting the lowest error rates. In addition, XGB-MPA has performed better than XGB in all scenarios, with the lowest and highest reduction in RMSE being 19.3% (scenario 5) and 97.4% (scenario 1), respectively. When comparing the performance of hybrid models, the proposed XGB-MPA model has performed best with MAE, RMSE and NSE of 40.94, 103.7 and 0.96, respectively, in comparison with 816.02, 1063.74 and −2.94 for XGB-PSO and 693.16, 981.68 and −2.37 for XGB-GWO. Further research can include the use of time series of efficient variables extracted from satellite images (e.g. land cover, river morphology, etc.) as model inputs. Highlights: Improvement of SL estimation by coupling MPA with XGB model Using delayed combinations of streamflow and sediment concentration as model inputs Superiority of MPA compare to PSO and GWO in SL estimation Greater variations in SHAP caused by sediment concentration compared to streamflow Further studies required on the effects of hydrological and topographical features.
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ArticleYalnız künye2024/r/ebyu/12441
Hybrid interpolation approach for estimating the spatial variation of annual precipitation in the Macta basin, Algeria
Achite, Mohammed; Katipoğlu, Okan Mert; Javari, Majid; Caloiero, Tommaso
Spatial precipitation analysis is essential for effectively managing hydrological modeling, construction of water structures, and irrigation planning. In this study, the ordinary kriging (OK), simple kriging (SK), global polynomial interpolation (GPI), local polynomial interpolation (LPI), inverse distance weighted (IDW), radial basis functions (RBF), and artificial neural network (ANN)-based hybrid techniques were compared to determine the spatial variation of annual precipitation. Statistical indicators derived from Willmott’s index of agreement, root mean square error, mean absolute percentage error, and the violin plot and boxplot graphical approaches were used to determine the most effective technique for precipitation interpolation. According to the analysis results, it has been observed that the ANN model significantly improves the prediction performance of single interpolation methods. The OK-ANN hybrid model was determined to be the most accurate representation of precipitation distribution, with the GPI-ANN model coming in second. The most precise results were obtained using the deterministic method, RBF with inverse multiquadric kernel function, LPI with Epanechnikov kernel function, and GPI with 3rd-order polynomial interpolations. In addition, it was determined that deterministic approaches produce more successful results than geostatistical approaches in the basin due to the presence of homogeneous and densely distributed meteorological observation networks.
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ArticleYalnız künye2024/r/ebyu/12385
Estimation 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.
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ArticleYalnız künye2024/r/ebyu/12253
A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network
Kilinc, Huseyin Cagan; Haznedar, Bulent; Katipoğlu, Okan Mert; Ozkan, Furkan
The management of water resources and the modeling of river flow have a prominent position within environmental research. They form a critical bridge between human societies and the delicate ecosystems they inhabit. Scholars have focused on benefiting more efficient methods based on the use of artificial intelligence for river flow forecasting, notably because modeling hydrological systems is quite challenging. This study primarily centered on exploring the predictive capacities of hybrid models in establishing a link between daily flow data and prospective data. In the study, the mentioned algorithms, firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), were hybridized with the artificial neural network (ANN) model and data analyzes were examined with the stations in the Konya Closed Basin. A comparative analysis of FFA–ANN, GA–ANN, ABC–ANN, and long short-term memory (LSTM) models was conducted for daily flow forecasting for daily flow forecasting according to a range of graphical and statistical metrics. The outcomes indicate that the FFA–ANN hybrid model generally performed better than other models and the deep learning algorithm.
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ArticleAçık Erişim2024/r/ebyu/12248
Short 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.
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ArticleAçık Erişim2024/r/ebyu/12222
Prediction 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.
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ArticleYalnız künye2024/r/ebyu/12144
Exploring 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.
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ArticleAçık Erişim2024/r/ebyu/12137
Comparative 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.
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ArticleYalnız künye2024/r/ebyu/12132
Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network
Katipoğlu, Okan Mert; Aktürk, Gaye; Kılınç, Hüseyin Çağan; Terzioğlu, Zeynep Özge; Keblouti, Mehdi
Suspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Yeşilırmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973–2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R2), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R2 of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R2 of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R2 of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures’ effective lifespan and operation and take precautions against sediment-related disaster risks.
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ArticleYalnız künye2024/r/ebyu/12129
Evaluating 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.
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