Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)

Abstract Evapotranspiration is an important component in planning and management of water resources. It depends on climatic factors and the influence of these factors on each other makes evapotranspiration estimation difficult. This study attempts to explore the possibility of predicting this important component using three different heuristic methods: support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). In this regard, according to the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations (Zahedan, Zabol, Iranshahr, and Chabahar) was calculated using monthly weather data. The weather data were then used as inputs to the SVM, ANFIS and GEP models to estimate potential evapotranspiration. Five different input combinations were tried in the applications. The results of SVM, ANFIS and GEP models were compared based on the coefficient of determination (R2), mean absolute error and root mean square error. Findings showed that the SVM model, whose inputs are average air temperature, relative humidity, wind speed, and sunny hours of the current and one previous month, performed better than the other models for the Zahedan, Zabol, Iranshahr, and Chabahar stations. Comparison of the three heuristic methods indicated that in all stations, the SVM, GEP and ANFIS models took first, second, and third place in estimation of the monthly potential evapotranspiration, respectively.

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Comparison of neural networks and neuro-fuzzy computing techniques for prediction of peak breach outflow

Journal of Hydroinformatics ◽

10.2166/hydro.2016.078 ◽

2016 ◽

Vol 18 (4) ◽

pp. 724-740 ◽

Cited By ~ 1

Author(s):

Hasan G. Elmazoghi ◽

Vail Karakale (Waiel Mowrtage) ◽

Lubna S. Bentaher

Keyword(s):

Fuzzy Inference ◽

Gene Expression Programming ◽

Absolute Error ◽

Coefficient Of Determination ◽

Regression Equations ◽

Data Set ◽

Inference System ◽

Embankment Dams ◽

Small Dams ◽

Neuro Fuzzy

Accurate prediction of peak outflows from breached embankment dams is a key parameter in dam risk assessment. In this study, efficient models were developed to predict peak breach outflows utilizing artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Historical data from 93 embankment dam failures were used to train and evaluate the applicability of these models. Two scenarios were applied with each model by either considering the whole data set without classification or classifying the set into small dams (48 dams) and large dams (45 dams). In this way, nine models were developed and their results were compared to each other and to the results of the best available regression equations and recent gene expression programming. Among the different models, the ANFIS model of the first scenario exhibited better performance based on its higher efficiency (E = 0.98), higher coefficient of determination (R2 = 0.98) and lower mean absolute error (MAE = 840.9). Moreover, models based on classified data enhanced the prediction of peak outflows particularly for small dams. Finally, this study indicated the potential of the developed ANFIS and ANN models to be used as predictive tools of peak outflow rates of embankment dams.

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Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods

Journal of Water Supply Research and Technology—AQUA ◽

10.2166/aqua.2019.062 ◽

2019 ◽

Vol 68 (7) ◽

pp. 573-584 ◽

Cited By ~ 2

Author(s):

Robabeh Jafari ◽

Ali Torabian ◽

Mohammad Ali Ghorbani ◽

Seyed Ahmad Mirbagheri ◽

Amir Hessam Hassani

Keyword(s):

Soft Computing ◽

Fuzzy Inference ◽

Gene Expression Programming ◽

Absolute Error ◽

Support Vector ◽

Eastern Region ◽

Data Set ◽

Inference System ◽

Groundwater Aquifer ◽

Tabriz Plain

Abstract Aquifers are one of the largest available freshwater resources. In this paper, total dissolved solids (TDS) of the groundwater aquifer in Tabriz plain is estimated by groundwater physicochemical parameters including Na, HCO3, Ca, Mg, and SO4 in the eastern region of Urmia Lake. For this purpose, four soft computing approaches, namely, multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and gene expression programming (GEP) were used to predict TDS for a period of 10 years (2002–2012). Data were collected from the East Azerbaijan Regional Water Organization, which totaled 1,742 samples. In the application, of the whole data set, 70% (1,220 samples) was used for training and 30% (522 samples) for testing. In the following, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) statistics were used for evaluating the accuracy of the models. According to the results, MLP, ANFIS, SVM, and GEP models could be employed successfully in estimating TDS alterations. A comparison was made between these soft computing approaches that corroborated the superiority of the GEP model over MLP, SVM, and ANFIS models with RMSE = 58.93, R = 0.998, and MAE = 5.21.

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Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

Sustainability ◽

10.3390/su12052022 ◽

2020 ◽

Vol 12 (5) ◽

pp. 2022 ◽

Cited By ~ 1

Author(s):

Kieu Anh Nguyen ◽

Walter Chen ◽

Bor-Shiun Lin ◽

Uma Seeboonruang

Keyword(s):

Machine Learning ◽

Random Forest ◽

Fuzzy Inference ◽

Absolute Error ◽

Organic Content ◽

Rank Test ◽

Support Vector ◽

Inference System ◽

Erosion Rates ◽

Northern Taiwan

This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

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Heuristic methods applied in reference evapotranspiration modeling

Ciência e Agrotecnologia ◽

10.1590/1413-70542018423006818 ◽

2018 ◽

Vol 42 (3) ◽

pp. 314-324 ◽

Cited By ~ 5

Author(s):

Daniel Althoff ◽

Helizani Couto Bazame ◽

Roberto Filgueiras ◽

Santos Henrique Brant Dias

Keyword(s):

Reference Evapotranspiration ◽

Performance Criteria ◽

Superior Performance ◽

Weather Data ◽

Coefficient Of Determination ◽

Accurate Estimation ◽

Support Vector ◽

Computational Techniques ◽

Heuristic Methods ◽

Scarce Data

ABSTRACT The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.

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Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge

Water Science & Technology Water Supply ◽

10.2166/ws.2020.211 ◽

2020 ◽

Vol 20 (8) ◽

pp. 3156-3171

Author(s):

Hiwa Farajpanah ◽

Morteza Lotfirad ◽

Arash Adib ◽

Hassan Esmaeili-Gisavandani ◽

Özgur Kisi ◽

...

Keyword(s):

Fuzzy Inference ◽

Gene Expression Programming ◽

Least Square ◽

Support Vector ◽

Discrete Wavelet ◽

Topsis Method ◽

M5 Model Tree ◽

Inference System ◽

Flow Discharge ◽

Discharge Temperature

Abstract This research uses the multi-layer perceptron–artificial neural network (MLP-ANN), radial basis function–ANN (RBF-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5T), gene expression programming (GEP), genetic programming (GP) and Bayesian network (BN) with five types of mother wavelet functions (MWFs: coif4, db10, dmey, fk6 and sym7) and selects the best model by the TOPSIS method. The case study is the Navrood watershed in the north of Iran and the considered parameters are daily flow discharge, temperature and precipitation during 1991 to 2018. The derived results show that the best method is the hybrid of the M5T model with sym7 wavelet function. The MWFs were decomposed by discrete wavelet transform (DWT). The combination of AI models and MWFs improves the correlation coefficient of MLP, RBF, LSSVM, ANFIS, GP, GEP, M5T and BN by 8.05%, 4.6%, 8.14%, 8.14%, 22.97%, 7.5%, 5.75% and 10% respectively.

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Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17103520 ◽

2020 ◽

Vol 17 (10) ◽

pp. 3520 ◽

Cited By ~ 11

Author(s):

Mohammed A. A. Al-qaness ◽

Ahmed A. Ewees ◽

Hong Fan ◽

Laith Abualigah ◽

Mohamed Abd Elaziz

Keyword(s):

Mean Squared Error ◽

Fuzzy Inference ◽

Absolute Error ◽

Coefficient Of Determination ◽

Percentage Error ◽

Inference System ◽

Infected People ◽

Marine Predators ◽

Forecasting Performance ◽

The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

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Assessment of global hydro-social indicators in water resources management

Scientific Reports ◽

10.1038/s41598-021-96776-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Omid Bozorg-Haddad ◽

Sahar Baghban ◽

Hugo A. Loáiciga

Keyword(s):

Water Resources ◽

Fuzzy Inference ◽

Human Life ◽

Gene Expression Programming ◽

Social Indicators ◽

Absolute Error ◽

Computing Methods ◽

Climatic Conditions ◽

Inference System ◽

Spatial And Temporal Scales

AbstractWater is a vital element that plays a central role in human life. This study assesses the status of indicators based on water resources availability relying on hydro-social analysis. The assessment involves countries exhibiting decreasing trends in per capita renewable water during 2005–2017. Africa, America, Asia, Europe, and Oceania encompass respectively 48, 35, 43, 20, and 5 countries with distinct climatic conditions. Four hydro-social indicators associated with rural society, urban society, technology and communication, and knowledge were estimated with soft-computing methods [i.e., artificial neural networks, adaptive neuro-fuzzy inference system, and gene expression programming (GEP)] for the world’s continents. The GEP model’s performance was the best among the computing methods in estimating hydro-social indicators for all the world’s continents based on statistical criteria [correlation coefficient (R), root mean square error (RMSE), and mean absolute error]. The values of RMSE for GEP models for the ratio of rural to urban population (PRUP), population density, number of internet users and education index parameters equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe and Oceania, respectively. Scalable equations for hydro-social indicators are developed with applicability at variable spatial and temporal scales worldwide. This paper’s results show the patterns of association between social parameters and water resources vary across continents. This study’s findings contribute to improving water-resources planning and management considering hydro-social indicators.

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Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

Applied Sciences ◽

10.3390/app9153172 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3172 ◽

Cited By ~ 24

Author(s):

Hoang-Long Nguyen ◽

Thanh-Hai Le ◽

Cao-Thang Pham ◽

Tien-Thinh Le ◽

Lanh Si Ho ◽

...

Keyword(s):

Artificial Intelligence ◽

Support Vector Machine ◽

Mean Squared Error ◽

Fuzzy Inference ◽

Absolute Error ◽

Support Vector ◽

Inference System ◽

Stone Matrix Asphalt ◽

Hybrid Artificial Intelligence ◽

Stone Matrix

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

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Application of Adaptive Network-Based Fuzzy Inference System (ANFIS) for River Stage Prediction in a Tropical Catchment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.735.195 ◽

2015 ◽

Vol 735 ◽

pp. 195-199 ◽

Cited By ~ 4

Author(s):

Nadeem Nawaz ◽

Sobri Harun ◽

Amin Talei

Keyword(s):

Fuzzy Inference System ◽

Hydrological Modeling ◽

Fuzzy Inference ◽

Absolute Error ◽

Coefficient Of Determination ◽

Adaptive Network ◽

Arx Model ◽

Inference System ◽

River Stage ◽

Statistical Measures

Computational intelligence (CI) tools have been successfully applied in different fields with superior performances. Neuro-fuzzy system (NFS) is one the approach which combines the benefits of two powerful CI tools known as artificial neural networks (ANN) and fuzzy logic. Although NFS has attracted researchers in many areas of study, few of its applications have been undertaken in hydrological modeling. Adaptive Network-based Fuzzy Inference System (ANFIS) is so far the most established NFS technique and this study is an application of ANFIS in river stage prediction by using rainfall and stage antecedents as inputs in the tropical catchment of Bekok River in Malaysia. To evaluate the performance of the ANFIS model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The results of this study were evaluated based on several statistical measures such as coefficient of efficiency (CE), coefficient of determination (r2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed that ANFIS can successfully predict the river stage and it outperforms ARX model significantly. ANFIS was also found better in estimating peak river stages comparing to ARX model. This study demonstrates the auspicious potential of ANFIS in river stage modeling.

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A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran

Water Science & Technology Water Supply ◽

10.2166/ws.2018.062 ◽

2018 ◽

Vol 19 (1) ◽

pp. 165-178 ◽

Cited By ~ 12

Author(s):

Samad Emamgholizadeh ◽

Razieh Karimi Demneh

Keyword(s):

Suspended Sediment ◽

Sediment Load ◽

Fuzzy Inference ◽

Gene Expression Programming ◽

Coefficient Of Determination ◽

Suspended Sediment Load ◽

Inference System ◽

Sediment Rating Curve ◽

Intelligence Models ◽

Better Than

Abstract The estimation of the suspended sediment load in rivers is one of the main issues in hydraulic engineering. Different traditional methods such as the sediment rating curve (SRC) can be used to estimate the suspended sediment load of rivers. The main problem with this method is its low accuracy and uncertainty. In this study, the ability of three intelligence models namely: gene expression programming (GEP), artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) were compared with the SRC method. The daily flow discharge and sediment discharge at two hydrometric stations of the Kasilian and Telar rivers in the period of 1964–2014 were used to develop intelligence models. The performance of these methods indicated that all intelligence models give reliable results in the estimation of the suspended sediment load and their performance was better than the SRC method. Moreover, results showed that the GEP model with a high coefficient of determination (R2) and a low mean absolute error (MAE) was better than both the ANN and ANFIS models for the estimation of daily suspended sediment load of the two sub-basins of the Kasilian and Telar rivers.

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