River water level prediction in coastal catchment using hybridized relevance vector machine model with improved grasshopper optimization

Tao, Hai; Al-Bedyry, Najah Kadhim; Khedher, Khaled Mohamed; Shahid, Shamsuddin; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.jhydrol.2021.126477

Cited by 44 publications

(25 citation statements)

References 65 publications

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“…As a result, locating the OSH is the same as finding the support hyperplanes with the most significant margin distance. The following are the two support hyperplanes: (Chang and Lin, 2011;Tao et al, 2021).…”

Section: Optimal Separating Hyperplane In Linear Svmmentioning

confidence: 99%

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Al-Zubaidi

Al-Sulttani

Rabee

2022

IGJ

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Monitoring the propagation of dunes is essential for natural hazard management. Accurate dunes mapping is critical in this situation. Landscape elements such as vegetation, water, dunes, and built-up are commonly separated using spectral indices. The discovery of dune features using a spectral index is one of the most significant achievements in earth observation. In this research, it was suggested Drifting Sand Index (DSI) is a newly created index that can be used to extract the land of dunes. The DSI is calculated using the normalized difference between six Landsat-8 bands (B, R, G, NIR, SWIR-1, SWIR-2). The linear SVM algorithm was implemented using library (LibLINEAR) in the R software to calculate a new linear equation. Two versions of the index have been proposed, the first is the complete version (DSI-C), and the second is the reduced version (DSI-R). The suggested indices results were compared to four previously proposed spectral indices (NDSI-1, NDSI-2, CI, and NDSLI). The acquired results demonstrated that the DSI-C and DSI-R had a high ability to distinguish between sand and different land covers, such as vegetation, water bodies, and various soil types. The average overall accuracy for all levels of the DSI-R, DSI-C, NDSI-1, NDSI-2, CI, and NDSLI was 87.455%,83.4333%, 78.030%, 68.528%, 65.986%, and 56.490%, respectively. The average Kappa Coefficient for DSI-R, DSI-C NDSI-1, NDSI-2, CI, and NDSLI was 74.915%, 66.873%, 56.076%, 37.073%, 31.978%, and 13.011%, respectively.

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Section: Optimal Separating Hyperplane In Linear Svmmentioning

confidence: 99%

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Al-Zubaidi

Al-Sulttani

Rabee

2022

IGJ

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“…Of the recent exploration for the advanced versions of AI models, hybrid version where two or more models are integrated for the purpose of improving the learning process has been noticed remarkably (Ghaemi et al, 2021;Tao, Al-Bedyry, et al, 2021;Tao, Habib, et al, 2021). The capacity of the particle swarm optimization (PSO) algorithm was explored to improve the performance of the neuro-fuzzy-based group method of data handling (NF-GMDH) model for K x estimation in rivers (Najafzadeh & Tafarojnoruz, 2016).…”

Section: K X Estimation Using Artificial Intelligence Models: Literature Reviewmentioning

confidence: 99%

Estimation of natural streams longitudinal dispersion coefficient using hybrid evolutionary machine learning model

Goliatt

Sulaiman

Khedher

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient (K x ) is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of K x has been challenging for hydrologists due to the high stochasticity and non-linearity of this hydraulic-environmental parameter. This study presents a new hybrid machine learning (ML) model integrating a Gaussian Process Regression (GPR) and an evolutionary feature selection (FS) approach (i.e. Covariance Matrix Adaptation Evolution Strategy (CMAES)) to estimate K x in natural streams. The dataset consists of geometric and hydraulic river system parameters from 29 streams in the United States. The modeling results showed that the proposed model outperformed other models in the literature, producing more stable and accurate estimations. The FS approach evidenced the significance of the cross-sectional average flow velocity (U), channel width (B), and channel sinuosity σ to estimate the dispersion coefficient. In quantitative terms, the integrated GPR model with feature selection approach attained the minimum root mean square error (RMSE = 48.67) and maximum coefficient of determination (R 2 = 0.95). The proposed hybrid evolutionary ML model arises as robust, flexible and reliable alternative computer aid technology for predicting the longitudinal dispersion coefficient in natural streams.

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“…In recent years, intelligent models have been used extensively in modeling and predicting hydraulic and hydrological phenomena. For example, use of support vector machine to predict flood sensitivity (Choubin et al, 2018, Karami et al, 2022, using LSSVM, a new method for correcting runoff prediction error (Liu et al, 2021), using variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) to predict the water quality of the Yangtze River (Song et al, 2021), using whale optimization algorithm and combining it with LSSVM to subscale rainfall prediction under climate change conditions (Valikhan Anaraki et al, 2021), application of SVM and LSSVM to modeling of evaporation from open water surfaces (Farasat et al, 2021), using Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to improve the accuracy of SPI drought forecast index (Pham et al, 2021), precipitation modeling by optimizing performance and LSSVM (Azad et al, 2021), using complementary ensemble empirical decomposition models and combining it with LSSVM to predict runoff in the medium and long term (Ji et al, 2020), using MSPI and data DRIVEN methods to detect multivariate droughts in the short and long term (Aghelpour et al, 2021), comparison of SVM and ANFIS performance for daily dam water level forecasting (Hipni et al, 2013), using Extreme Learning Machine in SVM Composition to Predict Hydrological Flow Series (Atiquzzaman and Kandasamy 2018), using SVM to predict long-term flow (Lin et al, 2006), evaluation of SVM and ANN performance in runoff modeling (Behzad et al, 2009), using SVM to estimate soil moisture (Gill et al, 2007), using SVM to estimate and predict evaporation rate (Moghaddaminia et al, 2009), investigation of statistical subscales on daily rainfall using SVM (Chen et al, 2010), using SVM to simulate and analyze runoff and sediment (Misra et al, 2009), using SVM to predict scour on control grade structures (Goel and Pal., 2009), a comparative study between ANN and SVM performance for groundwater level prediction in coastal aquifers (Yoon et al, 2011), monthly forecast of evaporation using ANN and SVM (Tezel and Buyukyildiz, 2015), using ANFIS and SVM to predict the relationship between runoff-precipitation (Tasar et al, 2019), combining improved GOA and SVM algorithms to predict water levels in streams in coastal areas (Tao et al, 2021), combination of SVR and GOA for spatial prediction of flood occurrence in Qazvin plain…”

Section: Introductionmentioning

confidence: 99%

Estimation of urban flood volume using Low-Impact Development methods and machine learning approach.

DadrasAjirlou

Karami

Rezaei

2022

Preprint

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In recent years, the growth of urbanization has increased the impermeable levels and has caused an increase in the volume of floods and peak flood discharges. Many types of research have been done in the field of implementing environmentally friendly methods to make the urban environment more natural and at the same time be effective in controlling urban floods. The use of Low Impact Development (LID) methods is one such study. But choosing the best designs from among these strategies has always been a vital problem for urban water system designers. One of the goals researchers in the field of urban hydrology is to find methods to determine the required volume reduction. In the present study, the hydraulic behavior of the surface water collection system in the Golestan town of Semnan has been simulated using Storm Water Management Model (SWMM). In the following, the performance of implementing different proposed designs of three types of LID_ namely rain barrel (RB), infiltration trench (IT), and permeable pavement (PP) _ was investigated. These plans include seven general scenarios, each with ten different LID combinations. These plans include seven general scenarios, each with ten different LID combinations. The results of hydraulic studies indicate the effectiveness of the PP-RB scenario with an average reduction of 90% of peak discharge and an average reduction of 80% of total flood volume. Also, the weakest performance is related to the IT scenario with an average reduction of 60% of peak discharge and 47% of total flow volume. In this regard, the considering the importance of estimation of flood reduction, present paper introduces a novel approach for urban flood mitigation estimation. In this method, intelligent algorithms are used to perform flood calculation operations taking into account the percentage of LIDs (Low-Impact Developments) proposed. In this research, SVM (support vector machines), LSSVM (Least square support vector machines) and LSSVM-GOA (Least square support vector machines-grasshopper optimization algorithm) algorithms have been used. The allocated percentage area of different combinations of the used LIDs, and the reduced peak flow coefficient in each combination were considered as input data; and the reduced flood volume corresponding to each LID combination was used as the output data. This research has been conducted in Golestan town of Semnan city in Iran. The results obtained in this study indicate the success of these algorithms in predicting reduced flood volume. In the test period, the value of R2 index for LSSVM-GOA model (0.9896) compared to LSSVM (0.9266) and SVM (0.8990) intelligent models, indicates the high accuracy of this model in this period. Also, the values of R2 index for the other two algorithms indicate the adequacy of these algorithms in predicting the amount of reduced flood. Also in the training course, LSSVM - GOA model showed that with values of 0.0101 and 0.0185 for MAE and RMSE indices, respectively, has a higher predictive power. The values of these indices are 0.0268 and 0.0361 for LSSVM model and 0.0318 and 0.0434 for SVM model, respectively. According to the results, the use of intelligent algorithms can be introduced as an accurate tool in estimating and predicting reduced flood volume in urban basins.

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River water level prediction in coastal catchment using hybridized relevance vector machine model with improved grasshopper optimization

Cited by 44 publications

References 65 publications

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Sand Dunes Spectral Index Determination Using Machine Learning Model: Case study of Baiji Sand Dunes Field Northern Iraq

Estimation of natural streams longitudinal dispersion coefficient using hybrid evolutionary machine learning model

Estimation of urban flood volume using Low-Impact Development methods and machine learning approach.

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