Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran

Abstract In the current study, several soft-computing methods including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and hybrid wavelet theory-GEP (WGEP) are used for modeling the groundwater's electrical conductivity (EC) variable. Hence, the groundwater samples from three sources (deep well, semi-deep well, and aqueducts), located in six basins of Iran (Urmia Lake (UL), Sefid-rud (SR), Karkheh (K), Kavir-Markazi (KM), Gavkhouni (G), and Hamun-e Jaz Murian (HJM)) with various climate conditions, were collected during 2004–2018. The results of the WGEP model with data de-noising showed the best performance in estimating the EC variable, considering all types of groundwater resources with various climatic conditions. The Root Mean Squared Error (RMSE) values of the WGEP model were varied from 162.068 to 348.911, 73.802 to 171.376, 29.465 to 351.489, 118.149 to 311.798, 217.667 to 430.730, and 76.253 to 162.992 μScm−1 in the areas of UL, SR, K, KM, G, and HJM basins. The WGEP model's performance (R-values) for deep wells, semi-deep wells, and aqueducts of the areas of the KM basin associated with the arid steppe cold (Bsk) dominant climate classification was the best. Also, the WGEP's extracted mathematical equations could be used for EC estimating in other basins.

Download Full-text

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.

Download Full-text

Estimation of current-induced pile groups scour using a rule-based method

Journal of Hydroinformatics ◽

10.2166/hydro.2012.175 ◽

2012 ◽

Vol 15 (2) ◽

pp. 516-528 ◽

Cited By ~ 16

Author(s):

N. Ghaemi ◽

A. Etemad-Shahidi ◽

B. Ataie-Ashtiani

Keyword(s):

Soft Computing ◽

Fuzzy Inference ◽

Computing Methods ◽

Accurate Estimation ◽

Scour Depth ◽

Pile Groups ◽

Empirical Formulas ◽

Inference System ◽

Acceptable Risks ◽

Soft Computing Methods

Scour phenomenon around piles could endanger the stability of the structures placed on them. Therefore, an accurate estimation of the scour depth around piles is very important for engineers. Due to the complexity of the interaction between the current, seabed and pile group; prediction of the scour depth is a difficult task and the available empirical formulas have limited accuracy. Recently, soft computing methods such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the prediction of the scour depth. However, these methods do not give enough insight into the generated models and are not as easy to use as the empirical formulas. In this study, new formulas are given that are compact, accurate and physically sound. In comparison with the other soft computing methods, this approach is more transparent and robust. Comparison between the developed formulas and previous empirical formulas showed the superiority of the developed ones in terms of accuracy. In addition, the given formulas can be easily used by engineers to estimate the scour depth around pile groups. Moreover, in this study, design factors are given for different levels of acceptable risks, which can be useful for design purposes.

Download Full-text

A Comparison of Soft Computing Methods for the Prediction of Wave Height Parameters

Knowledge-Based Engineering and Sciences ◽

10.51526/kbes.2021.2.1.31-46 ◽

2021 ◽

Vol 2 (1) ◽

pp. 31-46 ◽

Cited By ~ 1

Author(s):

Rifat Tur ◽

Serbay Yontem

Keyword(s):

Soft Computing ◽

Wave Height ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Computing Methods ◽

Coastal Structures ◽

Inference System ◽

Neuro Fuzzy ◽

Black Sea Coast ◽

Soft Computing Methods

In the previous studies on the prediction of wave height parameters, only the significant wave height has been considered as the unknown parameter to be predicted. However, the other wave height parameters, which may be required for the design of coastal structures depending on their importance level, have been neglected. Therefore, in this study, novel soft computing methods were used to predict all wave height parameters required for the design of coastal structures. To this end, wave data were derived from a buoy located in Southwest Black Sea Coast. Then, Multi-layer Perceptron Neural Network (MLPNN) and Adaptive-Neuro Fuzzy Inference System (ANFIS) models were developed to predict wave height parameters. Various input combinations were selected to create seven different sub-models. These sub-models were applied using developed MLPNN and ANFIS models. Accuracy of sub-models were evaluated for each wave height parameters in terms of performance evaluation criteria. The results showed that the wave height parameters predicted by the MLPNN and ANFIS methods are similar and both methods yield results acceptable for design purposes. However, for maximum wave height, Hmax, ANFIS sub-model yields slightly better results.

Download Full-text

APPLICATION OF SOFT COMPUTING METHODS AND SPECTRAL REFLECTANCE DATA FOR WHEAT GROWTH MONITORING

IRAQI JOURNAL OF AGRICULTURAL SCIENCES ◽

10.36103/ijas.v50i4.751 ◽

2019 ◽

Vol 50 (4) ◽

Author(s):

Sharabiani l. & et a

Keyword(s):

Soft Computing ◽

Precision Agriculture ◽

Spectral Reflectance ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Computing Methods ◽

Growth Monitoring ◽

Inference System ◽

Climate Conditions ◽

Soft Computing Methods

Technology of precision agriculture has caused to the remote sensors development that compute Normalized Difference Vegetation Index (NDVI) parameters. Vegetation indices obtained from remote sensing data can help to summarize climate conditions. Artiﬁcial Neural Networks (ANNs), as a soft computing methods, are one of the most efﬁcient methods for computing as compared to the statistical and analytical techniques for spectral data. This study was employed experimental radial basis function (RBF) of ANN models and adaptive neural-fuzzy inference system (ANFIS) to design the network in order to predict the soil plant analysis development (SPAD), protein content and grain yield of wheat plant based on spectral reflectance value and to compare two models. Results indicated that the obtained results of RBF method with high average correlation coefficient (0.984, 0.981 and 0.9807 in 2015 for SPAD, yield and protein, respectively and 0.979, 0.9805 and 0.984 in 2016) and low RMSE (0.271, 103.315 and 0.111 in 2015 for SPAD, yield and protein, respectively and 0.407, 105.482 and 0.121 in 2016) has the high accuracy and high performance compared to ANFIS models.

Download Full-text

Water temperature prediction in a subtropical subalpine lake using soft computing techniques

Earth Sciences Research Journal ◽

10.15446/esrj.v20n2.43199 ◽

2016 ◽

Vol 20 (2) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Saeed Samadianfard ◽

Honeyeh Kazemi ◽

Ozgur Kisi ◽

Wen-Cheng Liu

Keyword(s):

Water Temperature ◽

Soft Computing ◽

Water Surface ◽

Mean Squared Error ◽

Fuzzy Inference ◽

Gene Expression Programming ◽

Absolute Error ◽

Inference System ◽

Neuro Fuzzy ◽

Soft Computing Techniques

Lake water temperature is one of the key parameters in determining the ecological conditions within a lake, as it influences both chemical and biological processes. Therefore, accurate prediction of water temperature is crucially important for lake management. In this paper, the performance of soft computing techniques including gene expression programming (GEP), which is a variant of genetic programming (GP), adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) to predict hourly water temperature at a buoy station in the Yuan-Yang Lake (YYL) in north-central Taiwan at various measured depths was evaluated. To evaluate the performance of the soft computing techniques, three different statistical indicators were used, including the root mean squared error (RMSE), the mean absolute error (MAE), and the coefficient of correlation (R). Results showed that the GEP had the best performances among other studied methods in the prediction of hourly water temperature at 0, 2 and 3 meter depths below water surface, but there was a different trend in the 1 meter depth below water surface. In this depth, the ANN had better accuracy than the GEP and ANFIS. Despite the error (RMSE value) is smaller in ANN than GEP, there is an upper bound in scatter plot of ANN that imposes a constant value, which is not suitable for predictive purposes. As a conclusion, results from the current study demonstrated that GEP provided moderately reasonable trends for the prediction of hourly water temperature in different depths. ResumenLa temperatura del agua es uno de los parámetros básicos para determinar las condiciones ecológicas de un lago, ya que está influenciada por procesos químicos y biológicos. Además, la exactitud en la predicción de la temperatura del agua es esencial para el manejo del lago. En este artículo se evalúa el desempeño de técnicas de soft computing como la Programación de Expresiones de Genes (PEG), que es una variante de la Programación Genética (PG), el Sistema Neuro-fuzzy de Inferencia Adaptativa (Anfis, en inglés) y las Redes Neuronales Artificiales (RNA) para predecir la temperatura del agua en diferentes niveles de una estación flotante del lago Yuan-Yang (YYL), en el centro-norte de Taiwán. Se utilizaron tres indicadores estadísticos, el Error Cuadrático Medio (ECM), el Error Absoluto Medio (MAE, en inglés) y el Coeficiente de Correlación (R) para evaluar el desempeño de las técnicas de computación. Los resultados muestran que la PEG es más exacta en la predicción de la temperatura del agua entre 0,2 y 3 metros de profundidad. Sin embargo, se evidencia una tendencia diferente a partir del metro de profundidad. A esta distancia de la superficie, las RNA son más exactas que la PEG y el Anfis. Los resultados de este estudio probaron claramente la usabilidad del PEG y las RNA en la predicción de la temperatura del agua a diferentes profundidades.

Download Full-text