JOURNAL of ENVIRONMENTAL ENGINEERING & LANDSCAPE MANAGEMENT - 32 - Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods

Aug 4, 2026

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Study of climate change dynamics in the lower Mahanadi River basin integrates observed records with climate model outputs. Historical precipitation and temperature data for 1979–2020 from IMD are combined with monthly projections from the CORDEX-SMHI-MIROC model retrieved via ESGF. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) forecast precipitation, Tmax, and Tmin under RCP 2.6, 4.5, and 8.5 scenarios, producing near-, mid-, and far-term trajectories. Performance evaluation shows Fbprophet and SARIMAX as best performers using metrics such as R2, RMSE, r, P-bias, and NSE. ArcGIS spatial analysis with IDW interpolation maps projection variability and supports comparisons across historical and future scenarios. The work also discusses uncertainties in historical data, socio-economic indicators, and unpredictable RCP pathways while proposing a machine-learning integration framework to enhance reliability, alongside monthly pattern insights.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Data and Methods

Bias correction and data integration

Machine learning models and evaluation

Results and Discussion

Forecasting under RCP scenarios

Decadal trends and spatial variations

Conclusion

ISSN: 1648-6897 /eISSN: 1822-4199 JOURNAL of ENVIRONMENTAL ENGINEERING & LANDSCAPE MANAGEMENT 2024 Volume 32 Issue 4 Pages 270–282 [https://doi.org/10.3846/jeelm.2024.22352](https://doi.org/10.3846/jeelm.2024.22352) TEMPERATURE AND PRECIPITATION PROJECTION IN THE LOWER MAHANADI BASIN THROUGH MACHINE LEARNING METHODS Deepak Kumar RAJ, Gopikrishnan T. Department of Civil Engineering, National Institute of Technology Patna, Patna, India | Highlights:
■ data integration: This study combines observed and climate model data to analyse climate change in the lower Mahanadi River basin;
■ bias correction: Innovative bias correction techniques enhance the accuracy of climate projections by rectifying simulated data;
■ effective forecasting: Four machine learning models forecast precipitation and temperature, with Fbprophet and SARIMAX standing out for their performance;
■ decadal trends: Decadal projections of precipitation and temperature patterns reveal shifts under different future scenarios;
■ visualizing climate shifts: Utilizing ArcGIS for spatial analysis, this study provides intuitive visualizations of projected climate variability, facilitating easy comparisons between historical and future scenarios. | | | | --- | --- | --- | | Article History:
■ received 20 January 2023
■ accepted 27 June 2024 | Abstract. This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE) . Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations. | | | Keywords: climate model, machine learning, precipitation, temperature, Mahanadi Basin. | | | | Corresponding author. E-mail: [dkraj.iitbhu2018@gmail.com](dkraj.iitbhu2018@gmail.com) | | | | 1. Introduction | | mental Panel on Climate Change [IPCC], 2022) . It is crucial to anticipate future climatic shifts, especially when | | In the present era, there is a heightened awareness and | | considering their effects on river basins. Climate change | | concern regarding climate change, a phenomenon that | | has long been recognized as a significant factor influenc- | | poses significant challenges and implications for the fu- | | ing hydrology and water resources. Understanding these | | ture. Climate change has the potential to exacerbate and | | impacts is essential for effective management and adap- | | prolong droughts or floods, leading to adverse impacts | | tation strategies. This recognition highlights the urgent | | on various aspects of society and the environment. This | | need for proactive measures to address the challenges | | heightened concern underscores the urgent need for proactive measures and strategies to mitigate and adapt to | | posed by climate change in w

FAQs

What data sources are integrated to project climate change in the lower Mahanadi Basin?

The study combines historical precipitation and temperature observations from IMD (1979–2020) with monthly climate model data from the CORDEX-SMHI-MIROC model obtained via ESGF.

Which machine learning models are used for forecasting precipitation and temperature?

Four models are applied: Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX, and their results are compared across RCP 2.6, 4.5, and 8.5 scenarios.

How are model performances evaluated and which models perform best?

Performance is assessed using R2, RMSE, r, P-bias, and NSE. Fbprophet and SARIMAX are identified as superior models based on these metrics.

How is the spatial pattern of future climate change visualized?

ArcGIS spatial analysis with IDW interpolation is used to reveal spatial variations in projected precipitation and temperature, enabling comparison of historical and future trends across the basin.

Aug 4, 2026

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