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

2021 ◽  
Vol 34 (3) ◽  
Author(s):  
Muhammad Waqas ◽  
Muhammad Shoaib ◽  
Muhammad Saifullah ◽  
Adila Naseem ◽  
Sarfraz Hashim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
River Basin ◽  
Streamflow Forecasting ◽  
Artificial Intelligence Techniques ◽  
Jhelum River ◽  
Jhelum River Basin

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.

scholarly journals Future climate change impacts on runoff of scarcely gauged Jhelum river basin using SDSM and RCPs

2021 ◽  
Author(s):  
Saira Munawar ◽  
Muhammad Naveed Tahir ◽  
Muhammad Hassan Ali Baig
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impacts ◽  
Baseline Period ◽  
Future Climate Change ◽  
Mean Annual Precipitation ◽  
Climatic Data ◽  
Global Issue ◽  
Jhelum River ◽  
Jhelum River Basin

Abstract Climate change is a global issue and causes great uncertainties in runoff and streamflow projections, especially in high-altitude basins. The quantification of climatic indicators remains a tedious job for the scarcely gauged mountainous basin. This study investigated climate change by incorporating GCM (CCSM4) using the SDSM method for RCPs in the Jhelum river basin. Historical climatic data were coupled with Aphrodite data to cope with the scarcity of weather stations. SDSM was calibrated for the period 1976–2005 and validated for the period 2006–2015 using R2 and RMSE. Future climatic indicators were downscaled and debiased using the MB-BC method. The de-biased downscaled data and MODIS data were used to simulate discharge of Jhelum river basin using SRM. Simulated discharge was compared with measured discharge by using Dv% and NSE. The R2 and RMSE for SDSM range between 0.89–0.95 and 0.8–1.02 for temperature and 0.86–0.96 and 0.57–1.02 for precipitation. Projections depicted a rising trend of 1.5 °C to 3.8 °C in temperature, 2–7% in mean annual precipitation and 3.3–7.4% in discharge for 2100 as compared to the baseline period. Results depicted an increasing trend for climatic indicators and discharge due to climate change for the basin.

scholarly journals Spatial and temporal hydro-climatic trends in the transboundary Jhelum River basin

2017 ◽  
Vol 8 (3) ◽  
pp. 423-440 ◽  
Author(s):  
Rashid Mahmood ◽  
Shaofeng Jia
Keyword(s):  
River Basin ◽  
Stream Flow ◽  
Land Use Changes ◽  
Maximum Temperature ◽  
Regression Methods ◽  
Climatic Trends ◽  
Jhelum River ◽  
Increasing Trends ◽  
Jhelum River Basin ◽  
Operation And Management

The present study examined the hydro-meteorological trends and their magnitudes using the Mann–Kendall, Sen's slope, and linear regression methods in the Jhelum River basin. Maximum and minimum temperatures showed increasing trends in the basin. However, the increasing trends of maximum temperature in all seasons as well as in annual datasets were stronger and statistically more significant than minimum temperature. Precipitation showed non-significant increasing and decreasing trends spread evenly throughout the basin. However, decreasing trends dominated in the basin, except in winter, with an average annual decrease of 3.3 mm. In case of streamflow, seasonal and annual decreasing trends dominated in the basin. Summer showed stronger and significant decreasing trends at most of the hydrometric stations in the basin. An annual decrease of 8 mm was observed at Azad Pattan. These decreasing trends are most probably due to decreasing trends in precipitation and increasing trends in temperature, though other factors such as land use changes, industrialization, and urbanization can also affect the changes in streamflow. These decreasing trends in precipitation and stream flow can have some serious implications in the reduction of water availability to the Mangla reservoir, thus producing many challenges for efficient reservoir operation and management.

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