Mohammadi, Babak; Abdallah, Mohammed; Oucheikh, Rachid; Katipoğlu, Okan Mert; Cheraghalizadeh, Majid

Drought is a significant natural hazard that severely challenges water resource management and agricultural sustainability. This study aims to propose a novel approach for predicting streamflow drought indices (SDI-3, SDI-6, and SDI-12) in humid continental (Stockholm) and semi-arid (ELdiem) climates at different time-steps. The approach utilizes a Quantile Regression Neural Network (QRNN) coupled with wavelet decomposition (WD) techniques. Six mother wavelets (haar, sym8, coif5, bior6.8, demy, and db10) were used to decompose the SDI time series into different frequency bands, helping to identify patterns and trends in drought signals. The QRNN was compared with a tree-based machine learning (ML) model and two deep learning models: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Results from stand-alone models showed that the LSTM model outperformed others in predicting SDI-3, while the QRNN model performed best in predicting SDI-6 and SDI-12 in both study regions. In the Stockholm station, the hybrid models achieved acceptable accuracy with bior6.8-LSTM2 (Nash–Sutcliffe efficiency (NSE) = 0.927), bior6.8-QRNN2 (NSE = 0.962), and demy-QRNN2 (NSE = 0.984) performing best for SDI-3, SDI-6, and SDI-12 predictions during the test phase, respectively. For the ELdiem station, the db10-QRNN3 (NSE = 0.926), demy-QRNN3 (NSE = 0.934), and demy-QRNN2 (NSE = 0.981) models demonstrated superior performance during the test phase in predicting SDI-3, SDI-6, and SDI-12, highlighting the robust capability of hybrid models across two case studies. The results indicate that combining WD with ML models can produce more accurate hydrological drought predictions than traditional models.

Karsli, Fevzi; Ozdemir, Emirhan; Bahadir, Murat

The increasing use of Light Detection and Ranging (LiDAR) point clouds in vector map production highlights the need for a generalized approach to represent complex (linear and curvilinear) building boundary geometries, considering both technological advancements and architectural diversity. In this paper, the aim is to automatically regularize the complex geometric building boundaries extracted from LiDAR point clouds. In order to achieve this aim, building corner points were first detected with the Automatic Building Outline Regularization (ABORE) method. Then, a distance-based statistical method was implemented to determine the edge types representing the building boundary. If the building edges are linear, ABORE corner points are used. Conversely, if they are curvilinear, the edges are regularized using Bézier curves based on Bernstein polynomials. Thus, by generalizing the proposed ABORE method for regularizing only linear building boundaries, a novel method called Generalized Automatic Building Outline Regularization (G-ABORE) is developed to regularize building boundaries in complex geometry. The proposed method was implemented on the open-source Toronto, São Paulo, Minnesota, and locally produced Ordu LiDAR datasets. Regarding accuracy assessments, the overall performance of the novel method for building boundary regularization is 98.76%, 98.87%, 98.11%, and 98.99% for Completeness (Cp), Correctness (Cr), Quality (Q), and F-score metrics, respectively. In addition, RMSE and PoLiS positional accuracy values were calculated as 0.27 m and 0.24 m, respectively. The results demonstrate that the G-ABORE method effectively regularizes complex building boundaries with high accuracy. As a result, it is proven that the developed method can regularize building layers with an accuracy suitable for representation in vector maps, regardless of the structure of building boundary geometries extracted from point cloud data in fields such as architectural design, urban planning, and more.

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.

Samui, Pijush; Yesilyurt, Sefa Nur; Dalkilic, Huseyin Yildirim; Yaseen, Zaher Mundher; Roy, Sanjiban Sekhar; Kumar, Sanjay

River flow modeling is essential for critical aspects such as effective water management and structure planning, together with flood and drought analysis. There has been a growing interest in modeling hydrological systems via machine learning (ML) models. Various optimization techniques are utilized to develop the applications of ML-based hydrological models. The ultimate aim of this research is to establish high performance forecasting model. Therefore, this study conducts river flow modeling by using the daily data attained from a gauge station situated in the Euphrates Basin. For this purpose, Artificial Neural Network (ANN) model was hybridized with five different optimization algorithms i.e., Artificial Bee Colony (ABC), Teaching-Learning Based Optimization (TLBO), Ant Colony Optimization (ACO), Ant-Lion Optimization (ALO), and Imperialist Competitive Algorithm (ICA). In determining the inputs used to create the models, the distribution graph and correlation of the data with the previous period data were examined. The results were evaluated with eleven different statistical parameters and an error matrix. Examining the obtained results, the study revealed all models present high performance. When the results were reviewed in general, it was seen that all determination coefficient (R2) and Nash-Sutcliffe coefficient (NSE) values were higher than 0.962, and other parameters were very close to the optimum. By comparing all the developed hybrid models, ANN-ALO model reported the highest performance for river flow forecasting.

Katipoğlu, Okan Mert; Sarıgöl, Metin

In water resources engineering, streamflow estimation models are of great importance. The use of black box models in determining streamflow estimation is preferred because it saves time compared to deterministic models. In addition, the data needed is less in quality and quantity and has a lower transaction volume. Therefore, accurate flow estimation plays a crucial role in water resources planning, management, and sizing of water structures. This study aimed to forecast the monthly streamflow using particle swarm optimization, least-squares support-vector machine, and signal processing models. For this purpose, 575 monthly data between 1963-2010 were used in Sivas, and 622 monthly data between 1960-2011 were used in Kayseri. The model structure used past streamflow, temperature, and precipitation values as input. During the installation of hybrid models, the inputs are divided into subcomponents with three levels of discrete Meyer mother wavelet and presented to the LS-SVM model. The performance of the models was evaluated with the help of statistical indicators such as determination coefficient, mean absolute error, Nash–Sutcliffe Efficiency, mean square error, Violin plots and visual Taylor diagrams. At the end of the study, EMD-LSSVM was the best streamflow estimation model at station 1501, and W-LSSVM was the most successful streamflow estimation model at station 1535.