A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

Abstract

In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021–2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman–Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021–2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.

Machine Learning Strategy for Improved Prediction of Micronutrient Concentrations in Soils of Taif Rose Farms Based on EDXRF Spectra

“…The assessment provided in this study shows that the MLR, RF, and MARS have different performance predictions. In agreement with Kadkhodazadeh et al [59], our results indicated that MARS outperforms the other two algorithms (RF and MARS) when the number of explanatory variables is small. Other studies have suggested that the MARS model, as a non-parametric regression approach, has good potential for solving nonlinear problems with high dimensions [60,61].…”

“…This means the outputs of the RAE and RSE are real valued numbers ranging from zero to one, where zero implies that the model is performing optimally, and zero implies that the model is performing poorly. The last performance metric considered in this study is the R-Squared (R 2 ) which can be defined as [59]:…”

Optimizing ETo Prediction in the Mahanadi Basin: A Comprehensive Evaluation of Machine Learning Models with Emphasis on ANFIS Performance

“…Wang et al, 2022)(Mehdizadeh et al, 2021)(Aghelpour et al, 2022)(Kadkhodazadeh et al, 2022)(Adnan et al, 2021). These investigations have demonstrated the effectiveness of combining AI techniques for the accurate prediction of ETo, which aligns with the ndings of the present study.…”

Climate change and land-use change impacts on future availability of forage grass species for Ethiopian dairy systems

“…To evaluate the DM feed contribution, DM produced per individual grass species per estimated area was proportioned to total DM feed cattle requirement per region (%). The Uncertainty analysis of areas suitable for Buffel, Napier and Rhodes grass cultivation, under a 15% and 10% land availability scenario under three climate projection models was performed using Monte Carlo simulation model 68 . Based on the power of analysis 84% we selected distribution function to generate 100 new data points for each input.…”