Investigation of Hydraulic Jump Characteristics in Rough Beds Using M5 Model Tree = تحريات عن خصائص القفزة الهيدروليكية في أرضية خشنة باستخدام شجرة نموذج M5

2016 ◽  
Vol 12 (2) ◽  
pp. 631-648 ◽  
Author(s):  
Ghorban Mahtab ◽  
Mohammad Taghi Sattari
Keyword(s):  
Hydraulic Jump ◽  
M5 Model Tree ◽  
Model Tree ◽  
Rough Beds ◽  
M5 Model

Downscaling of precipitation on a lake basin: evaluation of rule and decision tree induction algorithms

2012 ◽  
Vol 43 (3) ◽  
pp. 215-230 ◽  
Author(s):  
Manish Kumar Goyal ◽  
C. S. P. Ojha
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Lake Basin ◽  
Monthly Precipitation ◽  
M5 Model Tree ◽  
Reanalysis Dataset ◽  
Model Tree ◽  
Basin Scale ◽  
M5 Model

We investigate the performance of existing state-of-the-art rule induction and tree algorithms, namely Single Conjunctive Rule Learner, Decision Table, M5 Model Tree, Decision Stump and REPTree. Downscaling models are developed using these algorithms to obtain projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these algorithms is evaluated through application to downscale the predictand for the Lake Pichola region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000 and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. M5 Model Tree algorithm was found to yield better performance among all other learning techniques explored in the present study. The precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.

Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting

2017 ◽  
Vol 553 ◽  
pp. 356-373 ◽  
Author(s):  
Mohammad Rezaie-balf ◽  
Sujay Raghavendra Naganna ◽  
Alireza Ghaemi ◽  
Paresh Chandra Deka
Keyword(s):  
Groundwater Level ◽  
M5 Model Tree ◽  
Model Tree ◽  
M5 Model

M5 model tree for pier scour prediction using field dataset

2012 ◽  
Vol 16 (6) ◽  
pp. 1079-1084 ◽  
Author(s):  
Mahesh Pal ◽  
N. K. Singh ◽  
N. K. Tiwari
Keyword(s):  
M5 Model Tree ◽  
Pier Scour ◽  
Model Tree ◽  
M5 Model

scholarly journals M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

2016 ◽  
Vol 06 (09) ◽  
pp. 1045-1054 ◽  
Author(s):  
R. Biabani ◽  
M. Meftah Halaghi ◽  
Kh. Ghorbani
Keyword(s):  
Temporal Evolution ◽  
Clear Water ◽  
M5 Model Tree ◽  
Model Tree ◽  
Abutment Scour ◽  
M5 Model

scholarly journals Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting: Rainfall and Runoff Prospective

2021 ◽  
Author(s):  
Muhammad Waqas ◽  
Muhammad Saifullah ◽  
Sarfraz Hashim ◽  
Mohsin Khan ◽  
Sher Muhammad
Keyword(s):  
Artificial Intelligence ◽  
Water Resources ◽  
Weather Forecasting ◽  
Rainfall Runoff ◽  
M5 Model Tree ◽  
Artificial Intelligence Techniques ◽  
Model Tree ◽  
Jhelum River ◽  
Jhelum River Basin ◽  
M5 Model

The forecasting plays key role for the water resources planning. Most suitable technique is Artificial intelligence techniques (AITs) for different parameters of weather forecasting and generated runoff. The study compared AITs (RBF-SVM and M5 model tree) to understand the rainfall runoff process in Jhelum River Basin, Pakistan. The rainfall and runoff of Jhelum river used from 1981 to 2012. The Different rainfall and runoff dataset combinations were used to train and test AITs. The data record for the period 1981–2001 used for training and then testing. After training and testing, modeled runoff and observed data was evaluated using R2, NRMSE, COE and MSE. During the training, the dataset C2 and C3 were found to be 0.71 for both datasets using M5 model. Similar results were found for dataset of C3 using RBF-SVM. Over all, C3 and C7 were performed best among all the dataset. The M5 model tree was performed better than other applied techniques. GEP has also exhibited good results to understand rainfall runoff process. The RBF-SVM performed less accurate as compare to other applied techniques. Flow duration curve (FDCs) were used to compare the modeled and observed dataset of Jhelum River basin. For High flow and medium high flows, GEP exhibited well. M5 model tree displayed the better results for medium low and low percentile flows. RBF-SVM exhibited better for low percentile flows. GEP were found the accurate and highly efficient DDM among the AITs applied techniques. This study will help understand the complex rainfall runoff process, which is stochastic process. Weather forecasting play key role in water resources management and planning.

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