An effective predictive model for daily evapotranspiration based on a limited number of meteorological parameters

Hameed, Mohammed Majeed; Khaleel, Faidhalrahman; Abed, Mustafa Abbas; Khaleel, Deiaaldeen; AlOmar, Mohamed Khalid

doi:10.1109/ieeeconf53624.2021.9668072

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…proposed the group method of data handling (GMDH) approach as a polynomial neural network to capture the complex relationship between the input and output in a nonlinear system [68]. Since having prior knowledge of the model is inconceivable in the mathematical model, the GMDH neural network (GMDH − NN) is utilized to overcome this issue [27]. As a result, in the GMDH − NN model, the simulation of complex systems can be carried out without needing any prior specialized knowledge.…”

Section: Group Methods Of Data Handling Ivakhnenko Firstmentioning

confidence: 99%

“…Machine learning (ML) has made tremendous progress in recent years in solving numerous engineering in general [27][28][29][30][31][32] and PM 2.5 concentration in particular [33][34][35][36][37][38][39][40][41][42]. ML combines data science, statistics, and computing in an interdisciplinary fashion.…”

Section: Previous Workmentioning

confidence: 99%

“…However, new versions of ANN modeling approaches such as ELM were not applied for air pollution forecasting. In addition, models such as ELM, GMDHNN, and GBR, despite their wide popularity in solving complex engineering problems [27,29,[60][61][62], were not used in previous works to predict the concentration of PM 2.5 . erefore, these modeling approaches and their capacity have been explored in more detail.…”

Section: Research Motivationmentioning

confidence: 99%

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The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

AlOmar

Khaleel

Al-Saadi

et al. 2022

Advances in Meteorology

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Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.

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Section: Group Methods Of Data Handling Ivakhnenko Firstmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Research Motivationmentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

AlOmar

Khaleel

Al-Saadi

et al. 2022

Advances in Meteorology

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“…Meanwhile, machine learning (ML) approaches have attracted much attention due to their superlative performance in dealing with high nonlinearity phenomena [17,18] and solving complex problems such as drought [19][20][21][22][23][24], rainfall [25][26][27][28][29], evapotranspiration [30][31][32][33][34] and streamflow [35][36][37][38]. For example, a study was conducted in the Queensland area where ML models' performances were compared with the Australian Predicted Ocean-Atmosphere Model (POAMA) for precipitation prediction.…”

Section: Introductionmentioning

confidence: 99%

Data-driven models for atmospheric air temperature forecasting at a continental climate region

et al. 2022

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Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.

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An effective predictive model for daily evapotranspiration based on a limited number of meteorological parameters

Cited by 2 publications

References 17 publications

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

Data-driven models for atmospheric air temperature forecasting at a continental climate region

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