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Assessment of Advanced Artificial Intelligence Techniques for Streamflow Forecasting in Jhelum River Basin

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: [email protected] 

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.

Figure 17-18
Figure 19-20-21
Figure 22-23
Equation 1-15

Pakistan Journal of Agricultural Research

September

Vol.37, Iss. 3, Pages 190-319

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