## 1 Introduction
Some studies have used this approach to predict BPH population abundance in both field and light trap catches [6; 7; 9; 26; 27; 28; 29; 30]. [PERSON], [PERSON], and [PERSON] [7] applied MLR and Geographic Information Systems with light trap and weather data to develop BPH outbreak potential zones at Maruteru in India. They used MLR pest-weather models to determine the risk of BPH outbreaks. [PERSON] [31] used the occurrence of small immature guajava (_Psidium Guajava_) fruits as a single and accurate indicator of host plant phenology variables and found that it was a more reliable indicator of changes in fruit fly (_Bactrocera dorsalis_) populations than variables. However, due to the nature of the linear relationships in the parameters, linear regression models may not provide accurate predictions in some complex situations such as with non-linear and extreme values data [32]. The artificial neural network (ANN) model is a tool that can model complex and non-linear relationships without any prior assumptions about the relations between independent and dependent variables. ANN is a type of machine learning model and is relatively competitive in terms of usefulness with conventional regression and statistical models. ANN has been successfully applied in various disciplines, such as agriculture, medical science, education, finance, management, security, engineering, trading commodity, and art [33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. For pest forecasting, ANN has been applied to model the pest population dynamics for the pod borer _Helicoverpa armigera_ (Lepidoptera: Nocutiade) [43], the fruit fly _Bactrocera dorsalis_ (Diptera: Tephritidae) [44], rice pests [45] and the paddy stem borer (_Sciropophaga intertulus_) [46]. In most of these studies, the authors referred to the limitation of linear multiple regression (MLR) and concluded that the ANN technique is more precise than MLR and is a good choice for predicting pest occurrence. However, ANN is unfamiliar to most entomology researchers so its use is not widespread in entomological research [46]. Random forest (RF) is an ensemble algorithm proposed by [PERSON] [47]. The algorithm combines bagging a sampling approach and the random selection of features to construct a collection of decision trees with controlled variation [48]. RF are fast and easy to implement, yields highly accurate predictions and can handle a very large number of input variables without overfitting [49]. This method has been implemented in many different fields in recent years [50] including crop pest and disease prediction. [PERSON] [51] applied several data mining techniques such as random forest, support vector machine, neural network, k-nearest neighbors, decision tree, and Gaussian naive bayes to predict grass grub damage and indicated that neural network and random forest performed slightly better than other classifiers. [PERSON] et al. [52] used RF to predict the nymphal stage percentages of the sumn pest in the Middle Eastern region and showed that their models have accuracy over 99% and also offer credible confidence intervals. However, the benefits of RF in predicting the crop pest population are still limited. This study focused on the use of ground-based weather parameters, time series of satellite-derived vegetation indices, and machine learning approaches. We aimed to: (1) investigate the association between lagged ground meteorological parameters and satellite-based rice phenology in relation to the temporal patterns of BPH population incidences; (2) compare models with only weather variables and models with a combination of weather and host-plant variables; (3) develop a predictive model for the population occurrence of BPH based on ANN and RF; and (4) evaluate the performance of the ANN and RF models using a traditional MLR model.