This review provides a detailed synthesis of various in-situ, remote sensing, and machine learning approaches to estimate soil moisture. Bibliometric analysis of the published literature on soil moisture shows that Time-Domain Reflectometry (TDR) is the most widely used in-situ instrument, while remote sensing is the most preferred application, and the random forest is the widely applied algorithm to simulate surface soil moisture. We have applied ten most widely used machine learning models on a publicly available dataset (in-situ soil moisture measurement and satellite images) to predict soil moisture and compared their results. We have briefly discussed the potential of using the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission images to estimate soil moisture. Finally, this review discusses the capabilities of physics-informed and automated machine learning (AutoML) models to predict surface soil moisture at higher spatial and temporal resolutions. This review will assist researchers in investigating the applications of soil moisture in the broad domain of earth sciences.

Node localisation is one of the significant concerns in Wireless Sensor Networks (WSNs). It is a process in which we estimate the coordinates of the unknown nodes using sensors with known coordinates called anchor nodes. Several bio-inspired algorithms have been proposed for accurate estimation of the unknown nodes. However, use of bio-inspired algorithms is a highly time-consuming process. Hence, finding optimal network parameters for node localisation during the network set-up process with the desired accuracy in a short time is still a challenging task. In this article, we have proposed an efficient way to evaluate the optimal network parameters that result in low Average Localisation Error (ALE) using a machine learning approach based on Support Vector Regression (SVR) model. We have proposed three methods (S-SVR, Z-SVR and R-SVR) based on feature standardisation for fast and accurate prediction of ALE. We have considered the anchor ratio, transmission range, node density and iterations as features for training and prediction of ALE. These feature values are extracted from the modified Cuckoo Search (CS) simulations. In doing so, we found that all the methods perform exceptionally well with method R-SVR outperforming the other two methods with a correlation coefficient (R = 0.82) and Root Mean Square Error (RMSE = 0.147m).

Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions. However, the major drawback of these techniques, including the manual selection of the optimal hyperparameters, is that they depend highly on the problem and demand application-specific expertise. In this paper, we introduced Automated Machine Learning (AutoML) model to automatically select the machine learning model (among support vector regression, Gaussian process regression, binary decision tree, bagging ensemble learning, boosting ensemble learning, kernel regression, and linear regression model) and to automate the hyperparameters optimisation for accurate prediction of numbers of k-barriers for fast intrusion detection and prevention using Bayesian optimisation. To do so, we extracted four synthetic predictors, namely, area of the region, sensing range of the sensor, transmission range of the sensor, and the number of sensors using Monte Carlo simulation. We used 80% of the datasets to train the models and the remaining 20% for testing the performance of the trained model. We found that the Gaussian process regression performs prodigiously and outperforms all the other considered explainable machine learning models with correlation coefficient (R = 1), root mean square error (RMSE = 0.007), and bias = − 0.006. Further, we also tested the AutoML performance on a publicly available intrusion dataset, and we observed a similar performance. This study will help the researchers accurately predict the required number of k-barriers for fast intrusion detection and prevention.