RBFNN-based model for heavy metal prediction for different climatic and pollution conditions

Elzwayie, Adnan; El-Shafie, Ahmed; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Afan, Haitham Abdulmohsin; Allawi, Mohammed Falah

doi:10.1007/s00521-015-2174-7

Cited by 51 publications

(23 citation statements)

References 22 publications

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“…(2) The SAD between estimated and observed discharges is computed based on the following equation: The following indexes are used for comparison of the different methods [35][36][37][38]:…”

Section: Hybrid Pso and Bamentioning

confidence: 99%

“…The Wilson flood was selected for comparative analysis because it has been widely studied in the literature, resulting in a comprehensive body of information for comparison between the new hybrid method and other methods. This is a benchmark experimental problem that was considered by Wilson [33][34][35][36][37]. The time flood is equal to 120 h, and the peak occurs at a time step of 60 h with a value of 85 cm.…”

Section: Case Studiesmentioning

confidence: 99%

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Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Ehteram

Othman

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2018

Water

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Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

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“…(2) The SAD between estimated and observed discharges is computed based on the following equation: The following indexes are used for comparison of the different methods [35][36][37][38]:…”

Section: Hybrid Pso and Bamentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Ehteram

Othman

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2018

Water

Self Cite

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“…In this study, time-series predictive techniques for water quality were discussed, which predict the value of water quality parameters at interval t through use of preceding time series, based on similar and other parameters. For many decades, numerous statistical analyses and AI-based modelling strategies have been used to influence time-series predictive techniques for water quality and in water resources management [6][7][8][9][10][11][12][13][14][15]. To this day, the term water quality is utilized for describing water conditions, such as physical, chemical, and biological properties.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios

Yahya

Ahmed

Othman

et al. 2019

Water

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Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

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“…Sivapragasam & Liong 18 , investigated the ability of the SVM method to predict streamflow Asefa et al 12 used SVM to predict seasonal and hourly multi-scale streamflow.Fuzzy set theory has been popularized as a method for streamflow forecasting in several research studies such as [19][20][21][22][23] . The main advantage of using a fuzzy system is considering the uncertainties in the modeling variables 24,25 . Different fuzzy-based models such as gradient least squares, batch least squares and adaptive neuro-fuzzy system (ANFIS) have been used in modeling engineering systems.…”

mentioning

confidence: 99%

“…In this context, there is a need to develop a special algorithm that can detect and select the optimal input pattern to develop a forecasting model for streamflow at a point along a river. Such an algorithm could search for the optimal input pattern to achieve high forecasting accuracy.Radial Basis Neural Network (RBNN) is a common method that applied as a predictor in several fields of mechanical, structural, physical, chemical and environmental using a simple and effective relation compare to the artificial intelligent-based neural network 7,24,[26][27][28][29][30][31] . In some time series problem the use of single predictor such as RBNN it might not promise to provide accurate results.…”

mentioning

confidence: 99%

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

Afan

Allawi

El-Shafie

et al. 2020

Sci Rep

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In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. the main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. this study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting. BackgroundThe inflow parameter is a significant component of the hydrological process in water resources. Accurate forecasting of river flows for long-term and short-term forecasts are crucial to solving different water engineering problems (e.g., designing agricultural land and flood protection works for urban areas) 1 . Accurate and reliable flow forecasting is a vital reference for making decisions in reservoir system control. Hence, streamflow forecasting modeling has attracted attention and great advances in this field have been developed in recent decades 2 .Conventional models (linear models) cannot capture the non-linearity and non-stationary of hydrological applications. The autoregressive moving average (ARMA) model, autoregressive model, and autoregressive integrated moving average (ARIMA) model are linear models that have been applied in hydrological time series forecasting 3-5 . The need for determining models capable of addressing the nonlinearity and non-stationary that are characteristics of natural reservoir inflow data has led researchers to propose advanced methods 6,7 . Recently, artificial intelligence methods showed relatively good forecasting accuracy. However, they had trouble detecting the highly stochastic pattern of the data.The most popular example of artificial intelligence methods is the artificial neural network (ANN). Wu et al. 8 established the Feed Forward Neural Network (FFNN) model for streamflow simulation. The finding evidenced the potential of the FFNN model for streamflow modeling. Two algorithms including multilayer perceptron (MLP) and radial basis function neural network (RBFNN) developed for river flow prediction 9 . The authors reported that the MLP model outperformed the RBNN model. Danandeh Mehr et al. 10 investigated the ability of successive station forecasting models using ANN in a rain gauge-...

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RBFNN-based model for heavy metal prediction for different climatic and pollution conditions

Cited by 51 publications

References 22 publications

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

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