Assessment of Advanced Artificial Intelligence Techniques for Streamflow Forecasting in Jhelum River Basin
Muhammad Waqas1*, Muhammad Shoaib2, Muhammad Saifullah1, Adila Naseem4, Sarfraz Hashim1, Farrukh Ehsan1, Irfan Ali3 and Alamgir Khan1
1Department of Agricultural Engineering, MNS-University of Agriculture Multan, Pakistan; 2Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan; 3Natural Resources Division, Pakistan Agricultural Research Council (PARC), Islamabad; 4Institute of Food Science and Nutrition, Bahauddin Zakariya University, Multan, Pakistan.
*Correspondence | Muhammad Waqas, Department of Agricultural Engineering, MNS-University of Agriculture Multan, Pakistan; Email: muhammad.waqas@mnsuam.edu.pk
Figure 1:
Map of Jhelum River basin, Western Himalayas.
Figure 2:
Flow chart of methodology.
Figure 3:
Single decision tree.
Figure 4:
Flow chart of decision tree forest.
Figure 5:
Flow chart of tree boost.
Figure 6:
Multi-Layer Perceptron Neural Network (MLPNN).
Figure 7:
Results of Co-efficient of determination (R2).
Figure 8:
Annual Results of Nash- Sutcliff Efficiency (NSE).
Figure 9:
Annual Results of Root Mean Square Error (RMSE).
Figure 10:
Seasonal results of coefficient of determination (R2) for training dataset.
Figure 11:
Seasonal results of coefficient of determination (R2) for testing dataset.
Figure 12:
Seasonal results of root mean square error (RMSE) for training dataset.
Figure 13:
Seasonal Results of Root Mean Square Error (RMSE) for Testing dataset.
Figure 14:
Seasonal Training Results of Nash- Sutcliff Efficiency (NSE).
Figure 15:
Seasonal Testing Results of Nash- Sutcliff Efficiency (NSE).
Figure 16:
Naran Flow duration curve.