Journal Article

Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques

Saboori Noghabi, Masoud, Momenzadeh, Hossein, Helali, Jalil, Asadi Oskouei, Ebrahim, Haddadi, Liza, Mianabadi, Ameneh, Mohammadi Ghaleni, Mehdi

2024

Overview

After precipitation, reference evapotranspiration (ET O ) plays a crucial role in the hydrological cycle as it quantifies water loss. ET O significantly impacts the water balance and holds great importance at the basin level because of the spatial distribution of managing water resources. Large scale teleconnection indices (LSTIs) play a vital role by influencing climatic variables and can be pivotal in determining ET O and its predictive variables. This study aimed to model and forecast annual ET O in Iran’s basins by utilizing LSTIs and employing various machine learning models (MLMs) such as least squares support vector machine, generalized regression neural network, multi-linear regression (MLR), and multi-layer perceptron (MLP). Initially, climate data from 122 synoptic stations covering six and 30, main and sub basins were collected, and annual ET O values were computed using the Food and Agriculture Organization 56 (PMF 56) Penman–Monteith equation. The correlations between these values and 37 LSTIs were examined within lead times ranging from 7 to 12 months. Through a stepwise approach, the most influential predictor indices (LSTIs) were selected as input datasets for the MLMs. The findings revealed the significant influence of factors such as carbon dioxide (CO 2 ), Atlantic multidecadal oscillation, Atlantic Meridional Mode, and East Atlantic on annual ET O . Overall, all MLMs performed well in terms of the Scatter Index during both training and testing phases across all sub-basins. Furthermore, the MLP and MLR models displayed superior performance compared to other models in the training and testing evaluations based on various assessment metrics.

• Publisher: Springer International Publishing,Springer Nature B.V,SpringerOpen
• Subject: Aquatic Pollution / Atlantic multidecadal oscillation / Basins / Basins of Iran / Carbon dioxide / Climate change / Climatic data / Comparative Law / Earth and Environmental Science / Earth Sciences / Evapotranspiration / Hydrogeology / Hydrologic cycle / Hydrological cycle / Industrial and Production Engineering / International & Foreign Law / Large-scale teleconnection indices / Learning algorithms / Machine learning / Machine learning models / Multilayer perceptrons / Multilayers / Nanotechnology / Neural networks / Original Article / Private International Law / Reference evapotranspiration / Regression analysis / Spatial distribution / Support vector machines / Teleconnections / Training / Waste Water Technology / Water balance / Water Industry/Water Technologies / Water loss / Water Management / Water Pollution Control / Water resources / Water resources management / Weather forecasting