Prediction of irrigation water quality indices based on machine learning and regression models
نویسندگان
چکیده
Abstract Assessing irrigation water quality is one of the most critical challenges in improving resource management strategies. The objective this work was to predict index Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. To achieve goal, three artificial intelligence (AI) models (Support vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression (Stepwise Regression, SW; Principal Components PCR; Partial least squares regression, PLS; Ordinary OLS) were applied validated for predicting six criteria (soluble sodium percentage, SSP; adsorption ratio, SAR; residual carbonate, RSC; potential salinity, PS; permeability index, PI; Kelly’s KR). Electrical conductivity (EC), (Na + ), calcium (Ca 2+ ) bicarbonate (HCO 3− used as input exploratory variables models. results indicated source not suitable without treatment. A good soil drainage system salinity control measures are required avoid salt accumulation within soil. Based performance statistics root mean square error (RMSE) scatter (SI), SW emerged best (0.21% 0.03%) followed by PCR PLS with RMSE 0.22% 0.21% SAR, respectively. classification SI, all having values less than 0.1 indicate prediction indices except RSC. These highlight using regressions developed machine learning methods quality, can be rapid decision tools modelling quality.
منابع مشابه
Thermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning
Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...
متن کاملThermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning
Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...
متن کاملeffect of seed priming and irrigation regimes on yield,yield components and quality of safflowers cultivars
این مطالعه در سال 1386-87 در آزمایشگاه و مزرعه پژوهشی دانشگاه صنعتی اصفهان به منظور تعیین مناسب ترین تیمار بذری و ارزیابی اثر پرایمینگ بر روی سه رقم گلرنگ تحت سه رژیم آبیاری انجام گرفت. برخی از مطالعات اثرات سودمند پرایمینگ بذر را بر روی گیاهان مختلف بررسی کرده اند اما در حال حاضر اطلاعات کمی در مورد خصوصیات مربوط به جوانه زنی، مراحل نموی، عملکرد و خصوصیات کمی و کیفی بذور تیمار شده ژنوتیپ های م...
thermal conductivity of water-based nanofluids: prediction and comparison of models using machine learning
statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. this paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. the thermal conductivity of nanofluids increases with the volume fraction and temperature. machine learni...
متن کاملapplication of upfc based on svpwm for power quality improvement
در سالهای اخیر،اختلالات کیفیت توان مهمترین موضوع می باشد که محققان زیادی را برای پیدا کردن راه حلی برای حل آن علاقه مند ساخته است.امروزه کیفیت توان در سیستم قدرت برای مراکز صنعتی،تجاری وکاربردهای بیمارستانی مسئله مهمی می باشد.مشکل ولتاژمثل شرایط افت ولتاژواضافه جریان ناشی از اتصال کوتاه مدار یا وقوع خطا در سیستم بیشتر مورد توجه می باشد. برای مطالعه افت ولتاژ واضافه جریان،محققان زیادی کار کرده ...
15 صفحه اولذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Water Science
سال: 2022
ISSN: ['2190-5495', '2190-5487']
DOI: https://doi.org/10.1007/s13201-022-01590-x