Reference Evapotranspiration Modeling Using New Heuristic Methods

The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

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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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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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Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

Mathematical Problems in Engineering ◽

10.1155/2020/4181045 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Amevi Acakpovi ◽

Alfred Tettey Ternor ◽

Nana Yaw Asabere ◽

Patrick Adjei ◽

Abdul-Shakud Iddrisu

Keyword(s):

Urban Areas ◽

Fuzzy Inference ◽

Predictive Accuracy ◽

Resource Planning ◽

Electricity Demand ◽

Least Square ◽

Quality Data ◽

Support Vector ◽

Inference System ◽

Neuro Fuzzy

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

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Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

10.20944/preprints202002.0075.v2 ◽

2020 ◽

Author(s):

Mahdi Ghadiri ◽

Azam Marjani ◽

Samira Mohammadinia ◽

Manouchehr Shokri

Keyword(s):

Relative Humidity ◽

Statistical Learning ◽

Air Conditioning ◽

Fuzzy Inference ◽

Least Square ◽

Support Vector ◽

Cooling Towers ◽

Learning Approaches ◽

Inference System ◽

Data Points

The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

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On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

BioMed Research International ◽

10.1155/2021/9202127 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Yong Yang ◽

Shuaishuai Zheng ◽

Zhilu Ai ◽

Mohammad Mahdi Molla Jafari

Keyword(s):

Mean Squared Error ◽

Fuzzy Inference ◽

Biogas Production ◽

Input Parameter ◽

Machine Learning Algorithms ◽

Least Square ◽

Support Vector ◽

Robust Algorithms ◽

Inference System ◽

Proposed Model

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

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A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

Shock and Vibration ◽

10.1155/2020/8835462 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Zhongbin Wang ◽

Bin Liang ◽

Lei Si ◽

Kuangwei Tong ◽

Chao Tan

Keyword(s):

Search Algorithm ◽

Optimal Parameter ◽

Gravitational Search Algorithm ◽

Principal Component ◽

Least Square ◽

Support Vector ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Recognition Method ◽

Mode Decomposition

The recognition of shearer cutting state is the key technology to realize the intelligent control of the shearer, which has become a highly difficult subject concerned by the world. This paper takes the sound signal as analytic objects and proposes a novel recognition method based on the combination of variational mode decomposition (VMD), principal component analysis method (PCA), and least square support vector machine (LSSVM). VMD can decompose a signal into various modes by using calculus of variation and effectively avoid the false component and mode mixing problems. On this basis, an improved gravitational search algorithm (IGSA) is designed by using the position update mechanism of Levy flight strategy to find the optimal parameter combination of VMD. Then, the feature extraction is achieved by calculating the envelope entropy and kurtosis of the decomposed intrinsic mode functions (IMFs). To avoid dimensional disasters and reinforce the classification performance, PCA is introduced to choose useful features, and the LSSVM-based classifier is reasonably constructed. Finally, the experimental results indicate that the proposed method is more feasible and superior in the recognition of shearer cutting states.

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

Water Science & Technology Water Supply ◽

10.2166/ws.2018.084 ◽

2018 ◽

Vol 19 (2) ◽

pp. 392-403 ◽

Cited By ~ 14

Author(s):

Omolbani Mohammadrezapour ◽

Jamshid Piri ◽

Ozgur Kisi

Keyword(s):

Fuzzy Inference ◽

Climatic Factors ◽

Potential Evapotranspiration ◽

Gene Expression Programming ◽

Absolute Error ◽

Weather Data ◽

Coefficient Of Determination ◽

Support Vector ◽

Heuristic Methods ◽

Inference System

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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