Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2540 - 2540

Published: Feb. 27, 2025

In this study, a comprehensive machine learning (ML) model was developed to predict and optimize boride coating thickness on steel surfaces based boriding parameters such as temperature, time, media, method, alloy composition. dataset of 375 published experimental results, 19 features were applied inputs the layer in various alloys. ML algorithms evaluated using performance metrics like Mean Absolute Error (MAE), Root Square (RMSE), R2. Among tested, XGBoost exhibited highest accuracy. achieved an R2 0.9152, RMSE 29.57, MAE 18.44. Incorporating feature selection categorical variables enhanced precision. Additionally, deep neural network (DNN) architecture demonstrated robust predictive performance, achieving 0.93. Experimental validation conducted 316L stainless (SS), borided at 900 °C 950 for 2 h 4 h. The DNN effectively predicted under these conditions, aligning closely with observed values confirming models’ reliability. findings underscore potential processes, offering valuable insights into relationships between outcomes, thereby advancing surface modification technologies.

Language: Английский

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Enhancing the prediction of irrigation demand for open field vegetable crops in Germany through neural networks, transfer learning, and ensemble models

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 312, P. 109402 - 109402

Published: March 18, 2025

Language: Английский

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Groundwater quality assessment for irrigation in coastal region (Güzelyurt), Northern Cyprus and importance of empirical model for predicting groundwater quality (electric conductivity)

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(8)

Published: April 1, 2025

Language: Английский

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Optimized intelligent learning for groundwater quality prediction in diverse aquifers of arid and semi-arid regions of India

Cleaner Engineering and Technology, Journal Year: 2025, Volume and Issue: 26, P. 100984 - 100984

Published: May 1, 2025

Language: Английский

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Assessment of groundwater quality and suitability for Irrigation purpose using Irrigation Indices, Remote Sensing and GIS approach

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101297 - 101297

Published: July 31, 2024

Language: Английский

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Assessment of Human Health Risk in Baitarani Basin, Odisha Using Water Quality Index (WQI), Cluster Analysis (CA), and Geographic Information Systems (GIS)

Published: Sept. 25, 2024

Language: Английский

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Surface water quality evaluation, apportionment of pollution sources and aptness testing for drinking using water quality indices and multivariate modelling in Baitarani River basin, Odisha

HydroResearch, Journal Year: 2024, Volume and Issue: 8, P. 244 - 264

Published: Dec. 10, 2024

Language: Английский

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Impacts of seasonal variations and wastewater discharge on river quality and associated human health risks: A case of northwest Dhaka, Bangladesh

Heliyon, Journal Year: 2023, Volume and Issue: 9(7), P. e18171 - e18171

Published: July 1, 2023

Surface water pollution caused by the discharge of effluents from industrial estates has become a major concern for Dhaka (Bangladesh). This study aims to have concise look at severe river pollution, mainly discharged tannery village. Effluent samples were collected five ejected points, including central effluent treatment plant (CETP), twenty adjacent water, and two pond nearby Hemayetpur, Savar. Thirty-one parameters been observed these sampling points three seasons, April 2021 January 2022. The results obtained quality indices, i.e., index (WQI), entropy (EWQI), irrigation (IWQI), show that most studied surface ranked "unsuitable" consumption, irrigation, anthropogenic purposes. highest health risk was downstream Hemayetpur city Savar CETP site, indicating higher levels heavy metal in following Carcinogenic non-carcinogenic human risks could be triggered consumption as concentrations arsenic (As), chromium (Cr), nickel (Ni), lead (Pb) exceeded upper benchmark 1 × 10−4 adults children. carcinogenic assessment revealed children more vulnerable hazards, quick corrective action is required control increased metals all sample locations. Therefore, through bioaccumulation, environment are affected areas. Using household work, or even purposes not advisable. study's result highlighted properly implementing compatible policies programs improve methods provide biodegradability Dhaleshwari River.

Language: Английский

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Maximum energy entropy: A novel signal preprocessing approach for data-driven monthly streamflow forecasting

Ecological Informatics, Journal Year: 2023, Volume and Issue: 79, P. 102452 - 102452

Published: Dec. 28, 2023

In recent years, the application of Data-Driven Models (DDMs) in ecological studies has garnered significant attention due to their capacity accurately simulate complex hydrological processes. These models have proven invaluable comprehending and predicting natural phenomena. However, achieve improved outcomes, certain additive components such as signal analysis (SAM) input variable selections (IVS) are necessary. SAMs unveil hidden characteristics within time series data, while IVS prevents utilization inappropriate data. realm research, understanding these patterns is pivotal for grasping implications streamflow dynamics guiding effective management decisions. Addressing need more precise forecasting, this study proposes a novel SAM called "Maximum Energy Entropy (MEE)" forecast monthly Ajichai basin, located northwestern Iran. A comparative was conducted, pitting MEE against well-known methods Discreet Wavelet (DW) Wavelet-Entropy (DWE), ultimately demonstrating superiority MEE. The results showcased superior performance our proposed method, with an NSE value 0.72, compared DW (NSE 0.68) DWE 0.68). Furthermore, exhibited greater reliability, boasting lower Standard Deviation 0.13 (0.26) (0.19). equips researchers decision-makers accurate predictions, facilitating well-informed water resource planning. To further evaluate MEE's accuracy using various DDMs, we integrated Artificial Neural Network (ANN) Genetic Programming (GP). Additionally, GP served method selecting appropriate variables. Ultimately, combination ANN forecasting model (MEE-GP-ANN) yielded most favorable results.

Language: Английский

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Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

Agronomy Journal, Journal Year: 2023, Volume and Issue: 116(3), P. 956 - 972

Published: Nov. 10, 2023

Abstract The scarcity of climatic data is the biggest challenge for developing countries, and development models reference evapotranspiration (ET 0 ) estimation with limited datasets crucial. Therefore, current investigation assessed efficacy four machine learning (ML) models, namely, linear regression (LR), support vector (SVM), random forest (RF), neural networks (NN), to predict ET based on minimal climate in comparison standard FAO‐56 Penman‐Monteith (PM) method. daily parameters were collected period 2000−2021, including maximum minimum temperatures ( T max min ), mean relative humidity R H wind speed W S sunshine hours SH ). performance developed considering different input combinations was evaluated by using several statistical measures. results showed that SVM model performed better than other ML during training 2 = 0.985; absolute error [MAE] 0.170 mm/day; square [MSE] 0.052 root [RMSE] 0.229 percentage [MAPE] 5.72%) testing stages MAE 0.168 MSE 0.050 RMSE 0.224 MAPE 5.91%) under full dataset scenario. best estimate , s . study are substantial as it offers an approach semi‐arid data‐scarce region.

Language: Английский

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