Hybrid Data Intelligent Models and Applications for Water Level Prediction

Yaseen, Zaher Mundher; Deo, Ravinesh C.; Ebtehaj, Isa; Bonakdari, Hossein

doi:10.4018/978-1-5225-4766-2.ch006

Cited by 10 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sigmoid activation function is chosen in this investigation based on its strong performance in previous studies reviewed in the literature. The function is described as follows (Azimi et al, 2017;Yaseen et al, 2018;Ebtehaj et al, 2019):…”

Section: Extreme Learning Machinementioning

confidence: 99%

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Zeynoddin,

Gumiere,

Bonakdari

2023

Front. Water

Self Cite

View full text Add to dashboard Cite

Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models.

show abstract

Section: Extreme Learning Machinementioning

confidence: 99%

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Zeynoddin,

Gumiere,

Bonakdari

2023

Front. Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are many activation functions, which include hard limit, triangular basis function, radial basis function, sine, and sigmoid. The sigmoid activation function [50][51][52] is employed in this study due to its successful performance in recent studies. The above-mentioned equation could be presented in the matrix form, as follows:…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

“…where D is the dimension of the problem. The DE algorithm is presented in [52]. However, the main steps of this algorithm are presented here briefly [53]:…”

Section: Differential Evolution (De)mentioning

confidence: 99%

Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

et al. 2019

View full text Add to dashboard Cite

Global solar radiation prediction is highly desirable for multiple energy applications, such as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables associated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the incorporation of all climate variables seemed to generate data intelligent models that performs with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m2]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions.

show abstract

Water management using genetic algorithm-based machine learning

et al. 2020

View full text Add to dashboard Cite

Hybrid Data Intelligent Models and Applications for Water Level Prediction

Abstract: Artificial intelligence (AI) models have been successfully applied in modeling engineering problems, including civil, water resources, electrical, and structure

Cited by 10 publications

References 47 publications

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

Water management using genetic algorithm-based machine learning

Contact Info

Product

Resources

About