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    Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Turkiye
    (Mdpi, 2023) Gul, Enes; Staiou, Efthymia; Safari, Mir Jafar Sadegh; Vaheddoost, Babak
    The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Turkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R-2) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Turkiye.
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    Ensemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approach
    (Irtces, 2023) Gul, Enes; Safari, Mir Jafar Sadegh; Dursun, Omer Faruk; Tayfur, Gokmen
    Uncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT); and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELMPSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUNbased hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling. (c) 2023 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.
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    Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport
    (Mary Ann Liebert, Inc, 2023) Gul, Enes; Safari, Mir Jafar Sadegh
    Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.
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    Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes
    (Springer Heidelberg, 2023) Kouzehkalani Sales, Ali; Gul, Enes; Safari, Mir Jafar Sadegh
    Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (t(s)) or deposited bed width (W-b) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of t(s) and W-b can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among W-b/Y, t(s)/Y, W-b/D, and t(s)/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as W-b/Y, t(s)/Y, W/D, and t(s)/D are considered for model development. It is found that models that incorporate sediment bed thickness (t(s)) provide better results than those which use deposited bed width (W-b) in their structures. Among four different scenarios, models that utilized t(s)/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.
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    Regression models for sediment transport in tropical rivers
    (Springer Heidelberg, 2021) Harun, Mohd Afiq; Safari, Mir Jafar Sadegh; Gul, Enes; Ab Ghani, Aminuddin
    The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.
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    Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms
    (Public Library Science, 2021) Gul, Enes; Safari, Mir Jafar Sadegh; Haghighi, Ali Torabi; Mehr, Ali Danandeh
    To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.1 84 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.
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    Studying the Changes in the Hydro-Meteorological Components of Water Budget in Lake Urmia
    (Amer Geophysical Union, 2022) Vaheddoost, Babak; Fathian, Farshad; Gul, Enes; Safari, Mir Jafar Sadegh
    Abrupt changes in the Lake Urmia water level have been addressed in many studies, and yet the link between the water level decline and hydro-meteorological variables in the basin is a major topic for debate between researchers. In this study, a set of data-driven techniques is used to investigate the components of the water budget in Lake Urmia. Then, the rate of monthly depth differences (DD), precipitation (P), evaporation (E), potential groundwater head (G), and streamflow (Q) time series between 1974 and 2014 are used in the analysis. Several scenarios and strategies are developed by considering the major changes in the year-2000, which is believed to be the initiation of the hydrological encroachment in the basin. Simple water budget (WB), dynamic regression (DR), and symbolic regression (SR) techniques are used to simulate the DD with consideration to P, E, G, and Q. Alternatively, the effect of the year 1997 as the potential base-line for the initiation of significant meteorological trends in the basin is investigated. Conducted analysis showed that the DR models of an autoregressive moving average together with multiple exogenous inputs provide an approximate R-2: 0.7 as the best alternative among the selected models. It is shown that the Q and G depict abrupt changes compared to the P and E, while either the year 1997 (climate effect) or the year 2000 (encroachment effect) is considered as the baseline in the study.
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    Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm
    (Springer Wien, 2021) Sales, Ali Kozekalani; Gul, Enes; Safari, Mir Jafar Sadegh; Ghodrat Gharehbagh, Hadi; Vaheddoost, Babak
    Lake water level changes are relatively sensitive to the climate-born events that rely on numerous phenomena, e.g., surface soil type, adjacent groundwater discharge, and hydrogeological situations. By incorporating the streamflow, groundwater, evaporation, and precipitation parameters into the models, Urmia lake water depth is simulated in the current study. For this, 40 years of streamflow and groundwater recorded data, respectively collected from 18 and 9 stations, are utilized together with evaporation and precipitation data from 7 meteorological stations. Extreme learning machine (ELM) is hybridized with four different optimizers, namely artificial bee colony (ABC), ant colony optimization for continuous domains (ACOR), whale optimization algorithm (WOA), and improved grey wolf optimizer (IGWO). In the analysis, 13 various scenarios with multiple input combinations are used to train and test the employed models. The best scenarios are then opted based on the performance metrics which are applied to assess the accuracy of the methods. According to the results, the hybrid ELM-IGWO shows better performance compared to the ELM-ABC, ELM-ACOR, and ELM-WOA approaches. Results indicate that the groundwater and persistence of the lake water depth have effective roles in models while incorporating higher number of variables can lower the performance of the models. Statistical analysis showed a 62% improvement in the performance of ELM-IGWO in comparison to the ELM-WOA with regard to the root mean square error. The promising outcomes obtained in this study may encourage the application of the utilized algorithms for modeling alternative hydrological problems.