Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations

Jafari, Mohammad Mahdi Molla; Ojaghlou, Hassan; Zare, Mohammad; Schumann, Guy Jean-Pierre

doi:10.3390/atmos12010009

Cited by 22 publications

(10 citation statements)

References 54 publications

(62 reference statements)

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“…The FCM approach is known as an improvement and modification of the well-known K-means clustering [9,10]. In this approach, each data point belongs partly to all clusters, however, with different membership degrees that can vary between 0 and 1.…”

Section: Estimation Of Rainfall Data Using the I-fcm Clustering Methodsmentioning

confidence: 99%

“…In this regard, this research introduces an innovative inverse Fuzzy C-Means clustering (i-FCM) method, which has been developed and applied as a spatial interpolation methodology to estimate rainfall data at locations with no measured rainfall, using measured data from nearby locations. Consequently, the widely and successfully applied LISFLOOD-FP flood model will be used to simulate flood events with different daily rainfall return periods (10,25, 50 and 100) in Dudelange, southern Luxembourg (ungauged location).…”

Section: Introductionmentioning

confidence: 99%

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Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

et al. 2021

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In this study, a new approach for rainfall spatial interpolation in the Luxembourgian case study is introduced. The method used here is based on a Fuzzy C-Means (FCM) clustering method. In a typical FCM procedure, there are a lot of available data and each data point belongs to a cluster, with a membership degree [0 1]. On the other hand, in our methodology, the center of clusters is determined first and then random data are generated around cluster centers. Therefore, this approach is called inverse FCM (i-FCM). In order to calibrate and validate the new spatial interpolation method, seven rain gauges in Luxembourg, Germany and France (three for calibration and four for validation) with more than 10 years of measured data were used and consequently, the rainfall for ungauged locations was estimated. The results show that the i-FCM method can be applied with acceptable accuracy in validation rain gauges with values for R2 and RMSE of (0.94–0.98) and (9–14 mm), respectively, on a monthly time scale and (0.86–0.89) and (1.67–2 mm) on a daily time scale. In the following, the maximum daily rainfall return periods (10, 25, 50 and 100 years) were calculated using a two-parameter Weibull distribution. Finally, the LISFLOOD FP flood model was used to generate flood hazard maps in Dudelange, Luxembourg with the aim to demonstrate a practical application of the estimated local rainfall return periods in an urban area.

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Section: Estimation Of Rainfall Data Using the I-fcm Clustering Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

et al. 2021

Self Cite

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“…On the other hand, FCM contributes a robust clustering strategy, dividing the dataset into fuzzy partitions based on similarity and utilizing membership functions to assign degrees of belongingness to each cluster. This data segmentation capability enhances the model's capacity to capture nuanced relationships within the sand-silt mixture data [67][68][69] . More specifically, FCM aims to cluster data points by iteratively updating the membership matrix based on the distances between data points and cluster centroids.…”

Section: Anfis With Fuzzy C-means (Anfis-fcm)mentioning

confidence: 99%

“…By combining adaptive neuro-fuzzy inference with data-driven clustering, ANFIS-FCM offers a comprehensive modeling ability that captures intricate relationships, providing a nuanced understanding of liquefaction resistance prediction. In the broader context, ANFIS-FCM contributes to the evolutionary approach of the hybrid model, showcasing its role in enhancing adaptability and efficiency in liquefaction resistance prediction [67][68][69] . The hybrid model, with its advanced optimization and predictive capabilities, offers improved accuracy and insights into the liquefaction resistance of sand-silt mixtures 70 .…”

Section: Anfis With Fuzzy C-means (Anfis-fcm)mentioning

confidence: 99%

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Hameed,

Masood,

Srivast

et al. 2024

Preprint

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Liquefaction is a devastating consequence of earthquakes that occur in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameters is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines the Extreme Learning Machine (ELM) with the Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.9%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored to the ELM-DOA model, to aid engineers and researchers in effectively utilizing the predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

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“…In another study, the Support Vector Machines (SVM) model was used to predict water levels in the Lanyang River in Taiwan for short term (1 to 6 hrs) [11]. The SVM least squares method was also used in predicting medium-and long-term runoff [12]. Nguyen et al [13] applied ML models such as LASSO, Random Forests and SVM to forecast daily water levels at Thakhek station on Mekong River.…”

Section: Introductionmentioning

confidence: 99%

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Hoan¹,

Dũng²,

Thu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

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Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations

Cited by 22 publications

References 54 publications

Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

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