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Rainfall-runoff modeling using machine learning in the ungauged urban watershed of Quetta Valley, Balochistan (Pakistan)

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Abstract

Quetta Valley is an integral part of the Pishin Lora Basin (PLB), a prominently water-deficient basin within the Balochistan province of Pakistan. The Pishin Lora River (PLR) flowing through the basin supports agriculture and domestic water use. However, the available scientific research on hydrological modeling in the study area is either limited or outdated. The influence of climate change on the basin's hydrology has intensified the demand for advanced approaches in flow prediction studies. Hydrological modeling is complex, particularly in a basin like Quetta with complicated terrain, including high elevations and varied slopes with diverse rainfall distribution. A machine learning (ML) data-driven approach is a viable alternative to conceptual hydrological models, mitigating the dependency on extensive data that may not always be readily available. This study employs Artificial Neural Network Multilayer Perceptron (ANN-MLP), Random Forest (RF), and Multiple Linear Regression (MLR) models for runoff prediction. Remotely sensed meteorological datasets spanning over thirty-two years (1990–2022) were obtained and compiled at three distinct timescales: daily, weekly, and monthly. The dataset comprises rainfall, humidity, and temperature records from the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) satellite. These variables serve as model inputs for predicting the runoff of the watershed. Simulated runoff data using the SCS-CN method were used in ML models in the absence of actual runoff records. Statistical tests were performed for the performance evaluation of the model, including coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and Willmott index (WI). Two input combinations (M1 and M2) were used to predict the daily, weekly, and monthly runoff timescales. The results of the all models were promising for both input combinations, with a slightly superior predictive performance observed in M2. In M2, the R2 values for ANN, RF, and MLR at the daily timescale were 0.998, 0.996, and 0.994, respectively. At the weekly timescales, these values were 0.994, 0.998, and 0.995, while at the monthly timescales, they were 0.995, 0.994, and 0.997. This study establishes improved water management strategies for data-scarce regions, exploiting the power of cutting-edge machine learning tools to drive innovation in hydrological research within the region.

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Data availability

No datasets were generated or analysed during the current study

Abbreviations

ANN:

Artificial neural network

MLP:

Multilayer perceptron

RF:

Random forest

MLR:

Multiple linear regression

PLB:

Pishin lora basin

PLR:

Pishin lora river

M1:

Model 1

M2:

Model 2

MAE:

Mean absolute error

RMSE:

Root mean squared error

R2 :

coefficient of determination

WI:

Wilmott Index

RAE:

Relative absolute error

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Acknowledgements

The authors are thankful to the Higher Education Commission, Islamabad for providing full financial support under Local Challenges Fund Project No. 20-LCF-63 titled; Metering the aquifer using a smart monitoring and data-driven approach to assist in devising an adaptive groundwater management strategy in Balochistan; and Mehran University of Engineering & Technology, Jamshoro, Pakistan for funding during the collection of literature data and the writing of this paper.

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The authors declare that they have not received any fund to conduct this research.

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Authors and Affiliations

  1. US Pakistan Center for Advanced Studies in Water (USPCASW), Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan

    Ghunwa Shah, Arjumand Zaidi, Abdul Latif Qureshi, Shahzad Hussain &  Rizwan

  2. Civil and Environmental Engineering, University Of Utah, Salt Lake City, UT, USA

    Tarique Aziz

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  1. Ghunwa Shah
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  2. Arjumand Zaidi
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Contributions

The authors confirm contribution to the paper as follows: study conception and design Ghunwa Shah, devised the research, the main conceptual ideas and proof outline and analysis and interpretation of results. Ghunwa Shah, Shahzad Hussain, Rizwan and Tariq Aziz worked out almost all of the technical details, and wrote the manuscript. Ghunwa Shah, Arjumand Zaidi, Abdul Latif Qureshi, Shahzad Hussain authors contributed to the final version of the manuscript.

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Correspondence to Ghunwa Shah.

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Communicated by H. Babaie.

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Shah, G., Zaidi, A., Qureshi, A.L. et al. Rainfall-runoff modeling using machine learning in the ungauged urban watershed of Quetta Valley, Balochistan (Pakistan). Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01302-w

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