Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

Figures & data

Figure 1. The study location and area.

Figure 2. NASA’s POWER Data Access Viewer.

Figure 3. Flowchart of ETo prediction algorithm for the prediction of ETo.

Figure 4. Demonstrate the methodology of the study.

Table 1. Modified Mann-Kendall test and Sen’s slope estimator results of ETo and climate variables on annual scale.

Variables	p-Value	Trend	Slope
Precipitation	0.413	No significant trend	0.001
Mean temperature	0.000	Increasing	0.021
Max temperature	0.011	Increasing	0.016
Min temperature	0.000	Increasing	0.030
Extraterrestrial solar radiation	0.605	No significant trend	0.000
Surface solar radiation	0.000	Increasing	0.088
Wind speed	0.696	No significant trend	0.000
Relative humidity	0.386	No significant trend	0.019
PM_ET0_mm	0.000	Increasing	0.012
BC_ET0_mm	0.000	Increasing	0.003
HA_ET0_mm	0.332	No significant trend	−0.001

Figure 5. The long period climate values of (A) precipitation (mm), (B) mean temperature (°C), (C) maximum temperature (°C), (D) minimum temperature (°C), (E) wind speed (m/s), (F) relative humidity (%) of study area from (1985–2022).

Figure 6. The long period climate values of (A) Extraterrestrial solar radiation (MJ/m2/day), (B) Surface solar radiation (MJ/m2/day), (C) reference evapotranspiration calculated by Penman-Monteith method (PM) (mm), (D) reference evapotranspiration calculated by Hargreaves method (HA) (mm), (E) reference evapotranspiration calculated by Blaney-Criddle method (BC) (mm of study area from (1985–2022).

Figure 7. Scatter plots showing.the comparison of predicted.and actual ETo values and their.relationship for (A) Penman-Monteith.method (PM) (mm), (B) by Hargreaves method (HA), and (C) Blaney-Criddle method (BC) during the testing period.

Table 2. Performance parameters for ETo methods using selected algorithms.

Methods	ML algorithms	Training				Testing
		R^2	RMSE (mm/day)	MAE (mm/day)	KGE	R^2	RMSE (mm/day)	MAE (mm/day)	KGE
PM	MLR	0.98	0.37	0.28	0.98	0.98	0.38	0.28	0.98
	KNN	0.99	0.24	0.17	0.99	0.98	0.28	0.19	0.98
	XGBoost	0.99	0.15	0.11	0.99	0.99	0.25	0.17	0.99
	SVR	0.99	0.22	0.14	0.99	0.99	0.24	0.15	0.99
	DT	0.98	0.30	0.22	0.99	0.97	0.39	0.29	0.98
	RF	0.99	0.23	0.15	0.99	0.98	0.26	0.18	0.99
HA	MLR	0.99	0.23	0.19	0.99	0.99	0.23	0.19	0.99
	KNN	0.99	0.01	0.01	0.99	0.99	0.06	0.04	0.99
	XGBoost	0.99	0.01	0.01	0.99	0.99	0.04	0.03	0.99
	SVR	0.99	0.05	0.04	0.99	0.99	0.05	0.04	0.99
	DT	0.99	0.13	0.10	0.99	0.99	0.17	0.13	0.99
	RF	0.99	0.20	0.15	0.99	0.99	0.21	0.16	0.99
BC	LR	0.92	0.35	0.29	0.94	0.92	0.35	0.30	0.94
	KNN	0.93	0.32	0.26	0.95	0.91	0.36	0.29	0.94
	XGBoost	0.93	0.33	0.27	0.95	0.92	0.35	0.29	0.94
	SVR	0.92	0.34	0.28	0.95	0.92	0.35	0.28	0.95
	DT	0.93	0.33	0.27	0.95	0.92	0.35	0.29	0.94
	RF	0.92	0.33	0.28	0.95	0.92	0.35	0.29	0.94

Data availability statement

Data that support the findings of this study are available from the corresponding author upon reasonable request.