Forecasting of Daily Pan Evaporation Rate using Deep Learning Techniques for Three Different Agro-Climatic Regions of Chhattisgarh State

Abstract

Abstract Accurate measurement or computation of evaporation loss is crucial for developing and successfully implementing water resource management strategies, irrigation planning, reservoir management, hydropower generation, drought and flood mitigation, urban planning and increasing agricultural productivity, especially in drought-prone areas. Evaporation can be measured directly using evaporimeters or forecasted using empirical models based on climatic variables such as temperature, humidity, wind speed, sunlight, and solar radiation, that influence the evaporation process. Modeling evaporation using climatic factors is difficult, especially when accounting for the wide range of agro-climatic conditions as it is an exceedingly nonlinear process. This paper uses different machine learning (ML) and deep learning algorithms to estimate pan evaporation (EP) for three distinct agro-climatic zones in the Indian state of Chhattisgarh. In this research, the performance of three machine learning models (Support Vector Machine, AdaBoost, and XGBoost) and four deep learning models (Deep Neural Network, Recurrent Neural Network, Long Short-Term Memory, and Bidirectional Long Short Term Memory) are evaluated and outcomes from each location are compared. Simulation results demonstrated that across all three regions, deep-Learning models outperform machine-learning and conventional models. Out of all deep learning models DRNN perform the best. As the results exhibit that the (EP) loss per day is less than 1 mm, the proposed model can be used for irrigation scheduling, water resource management which is very important for agriculture and its related activities.