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- ArticleYalnız künye2026Air quality index modeling using ensemble and data-intelligence modeling
Pande, Chaitanya Baliram; Katipoğlu, Okan Mert; Radwan, Neyara; Sidek, Lariyah Mohd; Pal, Subodh Chandra; Yaseen, Zaher Mundher; Ali, Tarig; Chakrabortty, Rabin; Refadah, Samyah Salem; Khan, Mohd Yawar Ali
Urban population growths, expansion of the industrial economy, burning stubble, and other major sources have significantly contributed to the increasing levels of air pollution in urban areas. Therefore, accurate prediction of the Air Quality Index (AQI) is essential for effective monitoring and planning within cities. In this research, an ensemble machine learning (ML) called Gradient Boosting (GB) model with 5- and 10-folds’ cross-validation was developed to achieve more precise and operative prediction outcomes based on important air quality related input variables. The results demonstrated that the suggested hybrid model attained the best accuracy in comparison with other benchmark ML models for predicting AQI values of Delhi city. Statistically, GB model reported second best model based on statistical metrics (R2 = 0.96, R = 0.98, RMSE = 23.03, MSE = 530.71, MAE = 15.16 and MARE = 11.96). Whereas Light Gradient Boosting Machine (LightGBM) model third best accuracy reported (R2 = 0.96, R = 0.98, RMSE = 23.21, MSE = 538.74, MAE = 14.68, and MARE = 11.33). The AQI prediction values are presented as spatial thematic maps for better understanding, highlighting city areas with high AQI levels to support air quality planning. The daily AQI prediction maps were prepared using various ML models. Further, the current research forecasted five days of AQI values and compared with the observed values. The results of five-day forecasting of GB model again indicated the best match with observed data of AQI values. Finally, forecasting accuracy results of GB (Boosting) models was attained 90% match with the observed values of AQI as compared with hybrid model. In conclusion, the reported results provided better understanding of AQI modeling for values and classes of spatial maps.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Beyond Conventional Methods: Predicting Novel Modified Groundwater Drought Indices with Advanced Computing Paradigms
Katipoğlu, Okan Mert; Kartal, Veysi; Aktürk, Gaye; Kilinc, Huseyin Cagan; Elshaboury, Nehal; Yamany, Mohamed S.; Pande, Chaitanya Baliram
To achieve accurate and reliable predictions of groundwater drought, it is essential to incorporate relevant meteorological variables and apply advanced predictive methodologies capable of modeling their complex interrelationships. In this study, groundwater drought prediction models were developed based on the seven modified drought indices, which are obtained from groundwater levels and multiple meteorological parameters. The analysis focuses on four districts in Türkiye (Tekirdağ-Çorlu, Tekirdağ-Çerkezköy, Amasya-Merzifon, and Amasya-Centre), which are hereafter referred to collectively as the study areas. Three machine learning models were applied such as a standalone Artificial Neural Network (ANN) and two hybrid models, ANN integrated with Shuffled Frog Leaping Algorithm (SFLA-ANN) and Firefly Algorithm (FFA-ANN). The hybrid metaheuristic algorithms were chosen for their ability to model nonlinear and complex relationships between meteorological variables and groundwater levels, which traditional statistical methods cannot fully capture. Modified drought indices were employed to represent the groundwater drought better. The findings indicate that the hybrid FFA-ANN model generally provides the highest predictive accuracy for most indices, with coefficient of determination values frequently exceeding 0.90. In contrast, the ANN model occasionally outperforms the hybrid FFA-ANN model in terms of root mean square error (RMSE) for specific stations within the study areas. These results highlight the robustness of hybrid ANN approaches, especially FFA-ANN, in predicting groundwater drought under changing climate conditions. The novelty of this study is the modification of meteorological drought indices widely used to represent groundwater drought and the application of hybrid metaheuristic ANN models to estimate these indices in Türkiye. The results demonstrated that such models provide a reliable framework for predicting groundwater drought and provide decision-makers with advanced tools for water resources management, irrigation planning, and climate adaptation strategies.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm
Saroughi, Mohsen; Katipoğlu, Okan Mert; Kartal, Veysi; Simsek, Oguz; Kilinc, Huseyin Cagan; Pande, Chaitanya Baliram
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood management. Direct measurement of SC is a challenging and expensive task. For these reasons, it was used to estimate the SC values at a station in the Euphrates River. New hybrid models were established by combining the CatBoost regressor (CBR) and artificial neural network (ANN) models with the honey badger optimization algorithm (HBA) and coati optimization algorithm (COA). The performance of the new model was compared with stand-alone model of ANN and CBR, and their accuracy was evaluated. In the setup of the models, 4 different input combinations of lagged sediment and discharge values for up to 3 months were evaluated. It is noteworthy that as the number of input variables, i.e., lagged data input, increases, the prediction accuracy of the models generally increases. HBA and COA algorithms often improve the accuracy of sediment prediction by optimizing the parameters of the single machine learning model. In addition, according to the AIC performance metric, the HBA algorithm is generally slightly better capable of optimization than the COA. The best model outputs were obtained according to the HBA-CBR hybrid approach of scenario 4 (RMSE = 59.78, AIC = 785.03, R2 = 0.32, PBIAS = 0.016, SI = 0.48, and MBE = − 2.05 in test phase), which consists of discharge and sediment with a delay of up to 3 months. The results of the study are valuable for decision-makers and planners in terms of practical reservoir and flood management and protection of coasts and river beds.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Application of Time Series Methodologies for Robust Forecasting of Atmospheric Pollutant Concentrations: PM10, SO2, NO2, NOx, and O3 in an Urban Environment
Katipoğlu, Okan Mert; Elshaboury, Nehal; Kartal, Veysi; Ertugay, Neşe; Kilinc, Huseyin Cagan; Şenocak, Serkan; Pande, Chaitanya Baliram
In recent years, the concentration of air pollutants has risen significantly due to urbanization and increased transportation. Erzincan province, due to its geographical location, is one of the region's most severely impacted by air pollution. Consequently, it is essential to accurately estimate air quality parameters to effectively analyze and manage the associated health risks. This study utilized a time series methodology to examine hourly air quality parameters, including particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitrogen oxides (NOx), and ozone (O3) throughout the year 2022 in Erzincan province. Various predictive techniques, such as feed-forward neural networks, group method of data handling, long short-term memory (LSTM), and least squares boosting tree methods, were employed for the analysis. For estimating air quality parameters, values delayed by up to 4 h—determined as the effective time period through correlation analysis—were utilized. Prediction performance was evaluated using eight different statistical parameters and visualization techniques. Notably, air quality parameters can be effectively estimated using historical data, with the LSTM model demonstrating superior performance compared to other models in this estimation. Furthermore, the most accurate predictions for O3 values were achieved using the LSTM algorithm, yielding an R2 of 0.93 and a root mean square error (RMSE) of 6.02. The findings of this study can aid policymakers in developing water resource management strategies, pollution control policies, and measures to combat climate change.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt
Elbeltagi, Ahmed; Vishwakarma, Dinesh Kumar; Katipoğlu, Okan Mert; Sushanth, Kallem; Heddam, Salim; Singh, Bhaskar Pratap; Shukla, Abhishek; Gautam, Vinay Kumar; Pande, Chaitanya Baliram; Hussain, Saddam
Air temperature plays a critical role in estimating agricultural water requirements, hydrological processes, and the climate change impacts. This study aims to identify the most accurate forecasting model for daily minimum (Tmin) and maximum (Tmax) temperatures in a semi-arid environment. Five machine learning models—linear regression (LR), additive regression (AR), support vector machine (SVM), random subspace (RSS), and M5 pruned (M5P)—were compared for Tmax and Tmin forecasting in Gharbia Governorate, Egypt, using data from 1979 to 2014. The dataset was divided into 75% for training and 25% for testing. Model input combinations were selected based on best subset regression analysis, result shows the best combination was Tmin(t−1), Tmin(t−3), Tmin(t−4), Tmin(t−5), Tmin(t−6), Tmin(t−7), Tmin(t−8) and Tmax (t−1), Tmax (t−2), Tmax (t−3), Tmax (t−4), Tmax (t−5), Tmax (t−6), Tmax (t−8) for daily minimum maximum air temperature forecasting, respectively. The M5P model outperformed the other models in predicting both Tmax and Tmin. For Tmin, the M5P model achieved the lowest root mean square error (RMSE) of 2.4881 °C, mean absolute error (MAE) of 1.9515, and relative absolute error (RAE) of 40.4887, alongside the highest Nash-Sutcliffe efficiency (NSE) of 0.8048 and Pearson correlation coefficient (PCC) of 0.8971. In Tmax forecasting, M5P showed a lower RMSE of 2.7696 °C, MAE of 1.9867, RAE of 29.5440, and higher NSE of 0.8720 and R² of 0.8720. These results suggest that M5P is a robust and precise model for temperature forecasting, significantly outperforming LR, AR, RSS, and SVM models. The findings provide valuable insights for improving decision-making in areas such as water resource management, irrigation systems, and agricultural productivity, offering a reliable tool for enhancing operational efficiency and sustainability in semi-arid regions. The Friedman ANOVA and Dunn’s test confirm significant differences among temperature forecasting models. Additive Regression overestimates, while Linear Regression and SVM align closely with actual values. Random Subspace and M5P exhibit high variability, with SVM differing significantly. For maximum temperature, Random Subspace and M5P perform similarly, while SVM remains distinct.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025A novel integrated approach to predict the sodium absorption ratio (SAR) of groundwater sustainability using deep learning models and SHAP approach
Moharir, Kanak N.; Pande, Chaitanya Baliram; Chakrabortty, Rabin; Pramanik, Malay; Paneerselvam, Balamurugan; Katipoğlu, Okan Mert; Pal, Subodh Chandra; Scholz, Miklas; Yadav, Krishna Kumar; Mansour, Lamjed
Agriculture is a crucial factor in improving the country economic growth. With the massive impact of environment variation, supplying and measuring the quality of water for irrigation usage is a crucial task for water resource management authority. The demand for good quality water for both drinking and irrigation uses is increasing day by day in recent years. The main aim of the present study is to identify the suitability of surface water for irrigation uses in the Man River basin of Maharashtra, India using advanced techniques. These region first time is used such kind of models i.e. boosted tree, AdaBoost, decision tree, extremely randomized tree model, and feed-forward neural network (deep learning) models, and these models are better to analyze and understand the groundwater quality datasets in the saline area. The prediction results are evaluated with the use of different performance metrics, mean absolute error (MAE), mean absolute relative error (MARE), Nash Sutcliffe efficiency (NSE), root mean squared error (RMSE), and coefficient of determination (R2). The study identified that the boosted tree model is more appropriate for the SAR prediction value, this model with high accuracy compared with other models. The result of this study shows that boosted tree model is very suitable for prediction of SAR and also provided the accurate information for agriculture purposes. In the first scenario, the boosted tree model shows lower value of mean squared error (MSE) of 0.26 and higher R2 of 0.88, the second scenario shows lower value of MSE of 0.11 and higher value of R2 of 0.91. Overall, in both the scenarios, the result of boosted tree model is more favorable for SAR% prediction. This work shows that machine and deep learning models can improve and better prediction of the groundwater quality in the study area. It is essential to understand the water quality and improve the sustainable agriculture system and development. These advanced modeling methods help to stakeholders make better water management and irrigation decisions, boosting agricultural sustainability and productivity.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2025Prediction of the monthly river water level by using ensemble decomposition modeling
Pande, Chaitanya Baliram; Sidek, Lariyah Mohd; Halder, Bijay; Katipoğlu, Okan Mert; Rajput, Jitendra; Alshehri, Fahad; Chakrabortty, Rabin; Pal, Subodh Chandra; Dom, Norlida Mohd; Scholz, Miklas
The decomposition, artificial intelligence (AI) and machine learning (ML) modeling have been important role in hydrological and river basin related prediction and forecasting to help the flood management and sustainable water resources development. In this paper, developed the hybrid modeling combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), along with standalone models support vector machine (SVM-linear), and Random Forest (RF), Random Subspace (RS) for accurate prediction of monthly river water level in Sg Muar at Buloh Kasap, Johor station during 2014 to 2023. In this paper, two combinations of variables such as Lags and IMFS are used for development of different models for river water level prediction. Hence, these models are compared and measured the performance of models based on the various statistics metrics. Therefore hybrid models performance is measured based on the coefficient of determination (R2), hence all models results are shown the CEEMDAN-SVM-LINEAR (R2 = 0.87), CEEMDAN-SVM-RBF (R2 = 0.91), CEEMDAN-RF (R2 = 0.98), and CEEMDAN-RS (R2 = 0.88) in the second combination variables, while standalone models performance are shown SVM-Linear (R2 = 0.84), SVM-RBF (R2 = 0.87), RF (R2 = 0.97), and RS (R2 = 0.86) during the training phase stage in the first combination variables. Similarly, in the testing phase, the best two models performances are very well as a CEEMDAN-RF (R2:0.94) and CEEMDAN-RS (R2:0.90) in second combination variables, and the first combination variables based SVM- Linear (R2:0.93) and RF (R2:0.89) models are performance higher compared with other models. Finally, the CEEMDAN-RF hybrid model is best model based on the lowest observed errors of Root mean square error (RMSE): 0.13, Mean square error (MSE): 0.02 and high R2: 0.94, hence this model is appropriate for prediction of river water level. Hence, the best hybrid model has been concluded that the CEEMDAN data decomposition technique is very useful for improve performance of the prediction model, the complex river water level predictions by separating the data sets into various sub-frequencies, allowing a better understanding of trends, seasonality and fluctuations in the data. Therefore, the CEEMDAN based novel hybrid modeling is effective decomposition modeling for complex field utilized in the sustainable and optimized utilization of the water resources for sustainable development goal (SDG).
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 Erişim2024Delineation of environmentally sustainable urban settlement using GIS-based MIF and AHP techniques
Liladhar Rane, Nitin; Purushottam Choudhary, Saurabh; Saha, Arjun; Srivastava, Aman; Pande, Chaitanya Baliram; Alshehri, Fahad; Roy, Ranjan; Katipoğlu, Okan Mert; Abdo, Hazem Ghassan
Choosing appropriate locations for built-up growth while maintaining natural balance has become necessary for sustainable urban planning. To this end, two multi-criteria decision-making (MCDM) approaches—the multi-influencing factor (MIF) and the analytical hierarchy process (AHP)—have been applied in this work. The MIF model results show that 23.546% of the study region was very highly suitable for newly built-up areas, and 18.786% was restricted for urban settlement, whereas, in the AHP model, 28.907% of the area was highly suitable, 17.619% was restricted for urban settlement. Finally, the dependability and efficiency of the two models used for the study area were assessed through the receiver operating curve (ROC) and area under curve (AUC) analysis. The comparative analysis demonstrates that the MIF approach (AUC 0.817) is more suitable and reliable than the AHP (AUC 0.783). The present work provides new insights to integrate GIS tools with MCDM for practical urban land suitability assessment.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2024Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River
Katipoğlu, Okan Mert; Kartal, Veysi; Pande, Chaitanya Baliram
The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.
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.
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