RETRACTED ARTICLE: Forecasting reference evapotranspiration using data mining and limited climatic data

Abstract

To accurate forecast of water evaporation and transpiration (reference evapotranspiration, ET 0) is imperative in the planning and management of water resources. The Penman-Monteith FAO56 (PM-56) equation which is recommended for estimating ET 0 across the world. However, it requires several climatic variables; the use of the PM-56 equation is restricted by the unavailability of input climatic variables in many locations. In the current study, the potential of k-Nearest Neighbor algorithm (KNN), which is a data mining method for estimating ET 0 were investigated using limited climatic data in a semi-arid environment in China. In addition, a KNN based ET 0 forecast model were tested against the PM-56 equation. The accuracies of the models were evaluated by using three commonly used criteria: root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (r). The results obtained with the KNN-based ET 0 forecast model (through normalization, weighted and K = 3) were better than it without any process. The prediction result is consistent with the PM-56 results, and confirmed the ability of these techniques to provide useful tools in ET 0 modeling in semi-arid environments. Based on the comparison of the overall performances, it was found that t the KNN-based ET 0 forecast model which requires max air temperature, min air temperature and relative humidity, input variables had the best accuracy.

RETRACTED ARTICLE: Guest editorial of the special issue “remote sensing in water management and hydrology”

“…The next article is entitled "Forecasting Reference Evapotranspiration Using Data Mining and Limited Climatic Data," and it is co-authored by Feng and Tian (2020). The authors make a more detailed research on seismic forward modeling of hydrothermal dolomite.…”

Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors

“…Numerous studies in the agriculture domain (Del Grosso et al 2008;Sun and Du 2017;) have focused on estimating crop-productivity variables (i.e., AET, RET, and NPP) from meteorological factors, and finding associations between them. However, there have been a few attempts at using traditional models (such as KNN (Feng and Tian 2021) and ARIMA (Landeras, Ortiz-Barredo, and López 2009)) and neural models (Alves, Rolim, and Aparecido 2017) to predict ET. These studies found that statistical/ML models are more accurate than Historical Average methods (which do not involve learning).…”

A Novel Approach in Prediction of Crop Production Using Recurrent Cuckoo Search Optimization Neural Networks

“…k-Nearest Nearest-Neighbor Algorithm Potentials, an ET0 estimation data mining tool, have been explored in semi-arid China, using minimal climate data [16]. Furthermore, the PM-56 equation was checked with a KNN-dependent ET0 prediction model.…”