Comparative Analysis of Inflow Forecasting using Machine Learning and Statistical Techniques: Case Study of Mangla Reservoir and Marala Headworks

Muhammad Muneeb Khan¹Muhammad Kaleem Sarwar^1*Muhammad Awais Zafar¹Muhammad Rashid²Muhammad Atiq Ur Rehman Tariq¹Saif Haider^1*Abdelaziz M Okasha³Ahmed Z Dewidar^4,5Mohamed A. Mattar^4,5*Ali Salem^6*

• ¹Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
• ²Department of Earth and Geoenvironmental Sciences, University of Bari Aldo Moro, VIA E. Orabona N. 4, Bari 70125, Italy
• ³Department of Agricultural Engineering, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt
• ⁴Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, P.O. Box 2454, Riyadh 11451, Saudi Arabia, Riyadh 11451, Saudi Arabia
• ⁵Department of Agricultural Engineering, College of Food and Agricultural Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia
• ⁶Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Pécs 7622, Hungary

This study, under the context of a global perspective, focuses on the Indus Basin Irrigation System (IBIS) of Pakistan specifically the Jhelum and Chenab rivers inflows. The IBIS operation relies on seasonal planning strategies, informed by forecasts of river inflows at key stations by the Indus River System Authority (IRSA). In this study, Artificial Intelligence (AI) models including Generalized Regression Neural Network (GRNN), and Multi-Layer Feedforward Neural Network (MLFN) along with the statistical model Autoregressive Integrated Moving Average (ARIMA) were used to forecast the inflows of both rivers for five years (2020 to 2024) with a lead time of one year. Historic flow data of 59 years (10 daily from 1966 to 2024) were collected from IRSA. The collected data from 1966 to 2014 are used for calibration/training and from 2015 to 2020 are used for validation/testing of selected models for both study locations. The results of correlation and error estimation depicted that Artificial Neural Network (ANN) models predicted better inflows than the ARIMA model. The average RMSE and R2 of ANN models is 9.68 and 0.92 and the average RMSE and R^2 of ARIMA Model is 10.17 and 0.88, this results in improvement of average RMSE and R^2 by 4.82 % and 4.35 % in case of ANN Models when compared with ARIMA Model. Qualitative analysis shows that ANN techniques better predicted the high and low flows when compared with statistical methods. Specifically, the application of the ANN models has enhanced the precision of forecasted inflows assessment compared to the probabilistic inflow forecasting methods used by IRSA. The average RMSE and R^2 in case of IRSA forecast is 11.47 and 0.88 and the average RMSE and R^2 in case of ANN Models is 10.30 and 0.92, this results in improvement of average RMSE and R^2 by 10.20 % and 4.35% in case of ANN Models when compared with IRSA forecast.. This study highlights the need for utilization of ANN models in place of probabilistic inflow forecasting methods to improve the accuracy of time series inflow forecasts.