Prediction of pan evaporation in Chhattisgarh using machine learning techniques

Accurate measurement or estimation of evaporation loss is crucial for developing and successfully implementing water resource management strategies, irrigation planning, reservoir management etc. To predict the pan evaporation (EP) accurately for Raipur, Jagdalpur, and Ambikapur stations of Chhattisgarh, four deep learning models and three machine learning models were used and a hybrid model using Deep Neural Network (DNN) and Random Forest (RF) was proposed. Simulation results demonstrated that the hybrid model (DNN+RF) outperforms the rest with R2 of 0.964, 0.920, 0.894 for Raipur, Jagdalpur and Ambikapur respectively. It has been observed that the hybrid DNN+RF model demonstrated faster convergence compared to other models with high accuracy, making it efficient and well-suited for real-time applications such as irrigation scheduling and water resource management.

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Publication Details

Journal
Journal of Agrometeorology
Published
2025-03-01
DOI
https://doi.org/10.54386/jam.v27i1.2731
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
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article

Prediction of pan evaporation in Chhattisgarh using machine learning techniques

R. Baloji Naik, Diwakar Naidu, Babita Majhi
Journal of Agrometeorology
Hydrological Forecasting Using AI
article

Prediction of pan evaporation in Chhattisgarh using machine learning techniques

R. Baloji Naik, Diwakar Naidu, Babita Majhi
article en

Abstract

Accurate measurement or estimation of evaporation loss is crucial for developing and successfully implementing water resource management strategies, irrigation planning, reservoir management etc. To predict the pan evaporation (EP) accurately for Raipur, Jagdalpur, and Ambikapur stations of Chhattisgarh, four deep learning models and three machine learning models were used and a hybrid model using Deep Neural Network (DNN) and Random Forest (RF) was proposed. Simulation results demonstrated that the hybrid model (DNN+RF) outperforms the rest with R2 of 0.964, 0.920, 0.894 for Raipur, Jagdalpur and Ambikapur respectively. It has been observed that the hybrid DNN+RF model demonstrated faster convergence compared to other models with high accuracy, making it efficient and well-suited for real-time applications such as irrigation scheduling and water resource management.

Journal of AgrometeorologyVol. 27(1)
Indira Gandhi Agricultural University (IN), Guru Ghasidas Vishwavidyalaya (IN)
Openalex Percentile: Top 98%
Hydrological Forecasting Using AI
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