Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis DOI Creative Commons
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Nov. 6, 2024

In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need potable water, contemporary purification technologies can be employed convert saline into supplies. prediction of important parameters desalination plants is key task designing and implementing these facilities. this regard, artificial intelligence techniques have proven powerful assets field. These methods offer an expedited effective means estimating parameters, thus catalyzing their implementation real-world scenarios. study, predictive accuracy six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive (AdaBoost), Categorical (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), Extremely Randomized Tree (ERT) was evaluated modelling parameter permeate flow as element system efficiency, energy consumption, quality using input combinations feed salt concentration, condenser inlet temperature, rate, evaporator temperature. The next phase research SHAP interpretability method illustrate impact individual variables on model's output. Moreover, performance developed frameworks set five dependable statistical measures: RMSE, NS, MAE, MAPE R2. indicators were utilized provide robust gauging precision forecasts. A comparative analysis outcomes, measured by RMSE criteria, revealed that SVR technique (RMSE = 0.125 L/(h·m2)) exhibited superior compared NGBoost 0.163 L/(h·m2)), AdaBoost 0.219 CatBoost 0.149 GPR 0.156 ERT 0.167 methodologies predicting rates. outcomes obtained during evaluation stage demonstrated efficacy algorithm enhancing forecasts, utilizing relevant variables.

Language: Английский

Evaluation of Solar Radiation Prediction Models Using AI: A Performance Comparison in the High-Potential Region of Konya, Türkiye DOI Creative Commons
Vahdettin Demir

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 398 - 398

Published: March 30, 2025

Solar radiation is one of the most abundant energy sources in world and a crucial parameter that must be researched developed for sustainable projects future generations. This study evaluates performance different machine learning methods solar prediction Konya, Turkey, region with high potential. The analysis based on hydro-meteorological data collected from NASA/POWER, covering period 1 January 1984 to 31 December 2022. compares Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), Artificial Networks (MLANN, RBANN). variables used include temperature, relative humidity, precipitation, wind speed, while target variable radiation. dataset was divided into 75% training 25% testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Square (RMSE), coefficient determination (R2). results indicate Bi-LSTM models performed best test phase, demonstrating superiority deep learning-based approaches prediction.

Language: Английский

Citations

1
Solar Activity Impact on Firefighter Interventions: Factors Analysis DOI
Naoufal Sirri, Christophe Guyeux

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 107 - 122

Published: Jan. 1, 2024

Language: Английский

Citations

6
Online sequential Extreme learning Machine (OSELM) based denoising of encrypted image DOI

Biniyam Ayele Belete,

Demissie Jobir Gelmecha, Ram Sewak Singh

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126999 - 126999

Published: Feb. 1, 2025

Language: Английский

Citations

0
Solar radiation prediction: A multi-model machine learning and deep learning approach DOI Creative Commons

C Vanlalchhuanawmi,

Subhasish Deb, Md. Minarul Islam

et al.

AIP Advances, Journal Year: 2025, Volume and Issue: 15(5)

Published: May 1, 2025

The increasing integration of renewable energies into electrical grids necessitates accurate forecasting meteorological variables, particularly solar irradiance. This study presents a novel long-term irradiance approach, utilizing data from the National Renewable Energy Laboratory spanning 1988–2022. Focusing on five input variables—solar irradiance, dew point, temperature, relative humidity, and wind speed—this evaluates predictive performance 13 data-driven models, comprising ten machine learning (ML) three deep (DL) algorithms. Among them, gradient boosting regressor (GBR) recurrent neural network (RNN) emerged as top performers in ML learning, respectively. In order to choose most suitable model for long short term, four forecast time-horizons (1, 8, 16, 24 h) were also taken consideration models. A feature selection process using Pearson’s coefficient identified relevant inputs, while quantile regression was employed uncertainty assessment, mean prediction interval, interval coverage probability demonstrates that RNN excels short-term predictions, GBR is more effective forecasts. new hybrid approach GBR-RNN developed, achieving superior terms RMSE, MAE, R2 metrics. multi-model integrating both DL techniques, enhances by addressing considering various horizons. findings contribute ongoing advancement energy providing robust, accurate, uncertainty-aware Moreover, this helps identify best-performing model, enabling reliable precise forecasts management. highlights improvement methods importance selecting best accuracy.

Language: Английский

Citations

0
An efficient data fusion model based on Bayesian model averaging for robust water quality prediction using deep learning strategies DOI
Meysam Alizamir,

Kayhan Moradveisi,

Kaywan Othman Ahmed

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 261, P. 125499 - 125499

Published: Oct. 15, 2024

Language: Английский

Citations

3
Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms DOI Creative Commons
Meysam Alizamir, Kaywan Othman Ahmed, Sungwon Kim

et al.

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(12), P. e0293751 - e0293751

Published: Dec. 27, 2023

Changes in soil temperature (ST) play an important role the main mechanisms within soil, including biological and chemical activities. For instance, they affect microbial community composition, speed at which organic matter breaks down becomes minerals. Moreover, growth physiological activity of plants are directly influenced by ST. Additionally, ST indirectly affects plant influencing accessibility nutrients soil. Therefore, designing efficient tool for estimating different depths is useful studies considering meteorological parameters as input parameters, maximal air temperature, minimal relative humidity, precipitation, wind speed. This investigation employed various statistical metrics to evaluate efficacy implemented models. These encompassed correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, absolute (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, GPR building robust predictive tools daily scale estimation 05, 10, 20, 30, 50, 100cm depths. The suggested models evaluated two stations (i.e., Sulaimani Dukan) located Kurdistan region, Iraq. Based on assessment outcomes study, exhibited exceptional capabilities comparison results showed that among proposed frameworks, yielded best depths, with RMSE values 1.814°C, 1.652°C, 1.773°C, 2.891°C, respectively. Also, 50cm depth, MLPANN performed 2.289°C station using during validation phase. Furthermore, produced most superior 10cm, 30cm, 1.753°C, 2.270°C, 2.631°C, In addition, 05cm SVR achieved highest level performance 1.950°C Dukan station. obtained research confirmed have potential be effectively used

Language: Английский

Citations

7
Performance of data-driven models based on seasonal-trend decomposition for streamflow forecasting in different climate regions of Türkiye DOI
Volkan Yılmaz, Cihangir Köyceğiz, Meral Büyükyıldız

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 136, P. 103696 - 103696

Published: Aug. 8, 2024

Language: Английский

Citations

2
Hybrid machine learning and optimization method for solar irradiance forecasting DOI
Chaoyang Zhu, Mengxia Wang,

Mengxing Guo

et al.

Engineering Optimization, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 36

Published: Sept. 4, 2024

Language: Английский

Citations

2
Efficient Computational Investigation on Accurate Daily Soil Temperature Prediction Using Boosting Ensemble Methods Explanation Based on SHAP Importance Analysis DOI Creative Commons
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103220 - 103220

Published: Oct. 1, 2024

Language: Английский

Citations

2
Characteristics of Land-Use Carbon Emissions and Carbon Balance Zoning in the Economic Belt on the Northern Slope of Tianshan DOI Open Access

Gulmira Abbas,

Alimujiang Kasimu

Sustainability, Journal Year: 2023, Volume and Issue: 15(15), P. 11778 - 11778

Published: July 31, 2023

How to identify variables for carbon reductions was considered as one of the most important research topics in related academic fields. In this study, characteristics landuse emissions economic belt on northern slope Tianshan (NST) were tentatively investigated. Taking 12 cities NST case land use and intensities estimated analyzed based Landsat remote sensing image socio-economic statistical data 1990, 2000, 2010, 2020. Moreover, Moran’s I model applied study spatial autocorrelation between intensities. Results show that (1) urban cropland increased rapidly during past three decades; (2) increasing significantly, responsible majority emission; (3) negative correlations both net obtained cities; (4) balance zoning analysis, could be divided into four different zones. The rising ratio significantly higher than urbanization expending speed. provide references useful insights arrangements policies attempts reduction NST.

Language: Английский

Citations

4