Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models

Ghorbani, MA; Khatibi, Rahman; Asadi, H.; Yousefi, Peyman

doi:10.5772/47801

Cited by 4 publications

(4 citation statements)

References 33 publications

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“…GEP is used to combine the models at Level 2 for modelling subsidence vulnerability. The modelling procedure in implementing GEP are the following: (i) select the fitness function, (ii) select terminal and function sets to generate the initial set of chromosomes, (iii) select the structure of the chromosomes, (iv) set their linking function and (v) select genetic operators (Ghorbani and Khatibi 2012).…”

Section: Artificial Intelligence Running Multiple Models By Gepmentioning

confidence: 99%

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

Nadiri

Khatibi²,

Khalifi

et al. 2020

Bull Eng Geol Environ

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Declining groundwater levels due to the absence of a planning system makes aquifers vulnerable to subsidence. This paper investigates possible hotspots in terms of Subsidence Vulnerability Indices (SVI) by applying the ALPRIFT framework, introduced recently by the authors by mirroring the procedure for the DRASTIC framework. ALPRIFT is suitable to cases, where data is sparse, and is the acronym of seven data layers to be presented in due course. It is a scoring technique, in which each data layer bears an aspect of land subsidence and is prescribed with rates to account for local variability, and with prescribed weights to account for relative significance of the data layer. The inherent subjectivity in prescribed weights is treated in this paper by learning their values from site-specific data by the strategy of using artificial intelligence to learn from multiple models (AIMM). The strategy has two levels: (i) at Level 1, three fuzzy models are used to learn weight values from the local data and from observed target data, and (ii) at Level 2, genetic expression algorithm (GEP) is used to learn further, in which the outputs of the models at Level 1 are reused as its inputs and observed data as its target values. The results show that (i) the Nash-Sutcliff Efficiency (NSE) coefficient for ALPRIFT with measured land subsidence values is approx. 0.21; (ii) NSE is improved to 0.88 by learning the weights at Level 1 using fuzzy logic, and (iii) NSE is further improved to 0.94 by further learning at Level 2 using GEP.

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Section: Artificial Intelligence Running Multiple Models By Gepmentioning

confidence: 99%

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

Nadiri

Khatibi²,

Khalifi

et al. 2020

Bull Eng Geol Environ

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“…Each figure represents the state of WDS demand at the given time. The evolution of phase space in this time series was given by reconstructing a pseudo phase space in which the demand of CKD, a nonlinear system, was considered by its self-interaction using AMI [43]. Figure 4c (τ =3) has a more regular pattern in comparison with the other two previous states of phase space (τ = 1, 2; Figure 4a and b, respectively), showing a lag time of 3 months to be optimum.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Soft Computing Models in Water Demand Forecasting

Shabani¹,

Yousefi²,

Adamowski³

et al. 2016

Water Stress in Plants

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Given the increasing trend in water scarcity, which threatens a number of regions worldwide, governments and water distribution system (WDS) operators have sought accurate methods of estimating water demands. While investigators have proposed stochastic and deterministic techniques to model water demands in urban WDS, the performance of soft computing techniques [e.g., Genetic Expression Programming (GEP)] and machine learning methods [e.g., Support Vector Machines (SVM)] in this endeavour remains to be evaluated. The present study proposed a new rationale and a novel technique in forecasting water demand. Phase space reconstruction was used to feed the determinants of water demand with proper lag times, followed by development of GEP and SVM models. The relative accuracy of the three best models was evaluated on the basis of performance indices: coefficient of determination (R 2 ), mean absolute error (MAE), root mean square of error (RMSE), and Nash-Sutcliff coefficient (E). Results showed GEP models were highly sensitive to data classification, genetic operators, and optimum lag time. The SVM model that implemented a Polynomial kernel function slightly outperformed the GEP models. This study showed how phase space reconstruction could potentially improve water demand forecasts using soft computing techniques.

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“…By aggregating data at increasing temporal scales, the effects of scaling time series on deterministic chaos can be found [54,55] and any missing data can be generated [56,57]. This approach has been used in solving problems in various fields of study such as river discharge [58][59][60], sedimentation [61][62][63], climate [64], lake level variability [65,66], rainfall [67][68][69][70], traffic speed [71], finance [72], image processing [73] and ship motion prediction [74]. However, there is a paucity of study on the application of chaos theory on water consumption forecasting methods.…”

Section: Introductionmentioning

confidence: 99%

“…Since GEP provides a tree-structure scheme, it makes GEP more convenient to interpret the results in comparison with GP. Moreover, GEP presents mathematical equations that clarify the relationship between input and output variables by a factor of 100-10000 [63,79,80]. The superiority of GEP and the advantages of this technique interested researchers to develop more sophisticated models with hybrid methods such as combining the extended Kalman filter [81], clustering the consumption values [82], Wavelet decomposition [39], and phase space reconstructed GEP (PSR-GEP) [42] in forecasting urban drinking water consumption.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

Yousefi

Courtice

Naser

et al. 2020

Water

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This study investigated urban water consumption complexity using chaos theory to improve forecasting performance to help optimize system management, reduce costs and improve reliability. The objectives of this study were to (1) investigate urban water distribution consumption complexity and its role in forecasting technique performance, (2) evaluate forecasting models by periodicity and lead time, and (3) propose a suitable forecasting technique based on operator applications and performance through various time scales. An urban consumption dataset obtained from the City of Kelowna (British Columbia, Canada) was used as a test case to forecast future consumption values using varying lead times under different temporal scales to identify models which may improve forecasting performance. Chaos theory techniques were employed to inform model optimization. This study attempted to address the paucity of studies on chaos theory applications in water consumption forecasting. This was accomplished by applying non-linear approximation, dynamic investigation, and phase space reconstruction for input variables, to improve the accuracy in various periodicity and lead time. To reconstruct the phase space, lag time was calculated using average mutual information for daily resolution as 17 days to reconstruct the phase space. The optimum embedding dimension and correlation exponent for the phase space were 18 and 3.5, respectively. Comparing the results, the non-linear local approximation model provided the best performance. The forecasting horizon for the models was 122 days. Moreover, phase space reconstruction improved the accuracy of the models for the different lead times. The findings of this study may improve forecasting performance and provide evidence to support further investigation of the chaotic behaviour of water consumption values over different time scales.

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Inter-Comparison of an Evolutionary Programming Model of Suspended Sediment Time-Series with Other Local Models

Cited by 4 publications

References 33 publications

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

Intelligent Soft Computing Models in Water Demand Forecasting

Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

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