Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation

Abstract

Soil moisture content (SMC) prediction can contribute to diverse geo-science engineering applications such as plantation, crops production, and several irrigation activities. Although, there have been several methodologies introduced for the SMS estimation, methods are still associated with challenges and limitations (e.g., time-consuming, high cost, and the need for large data sets). In the current research, the motivation was inspired to develop new data-intelligence models that are capable to estimate SMC accurately and reliably. Several hybridized adaptive neuro fuzzy inference system (NF) models were developed. The feasibility of four meta-heuristic algorithms including the gray wolf optimization (GWO), Bee algorithm (BA), Firefly algorithm (FA), and imperialistic competitive algorithm (ICA), were used to improve the predictability performance of the NF model. The performances of the hybrid models were tested at two different climate characteristics (i.e., humid and arid) and four different soil depths (5, 10, 20, and 50 cm). The dataset used in this study were consisted of ten years with daily scale. Practical inputs predictors for the prediction matrix were identified using the mutual information method (MI) and correlation coefficient, which included maximum and minimum temperature, solar radiation, and soil temperature. K-Fold technique and various indices were used to evaluate hybrid models. Despite the promising results of all developed models, the NF-FA is superior to the others in both climates. The average results of all depths (in humid climate) using correlation coefficient (r), mean absolute percentage error (MAPE), and scattering index (SI) were 0.924, 6.089%, and 0.074, respectively. While in the arid climate, NF-FA model attained 0.903, 14.439%, and 0.176 in r, MAPE, and SI, respectively. The evaluation of different depths and climates showed that NF-FA yielded the best accuracy within humid climates and depths of 20 and 10 cm had the lowest estimation error in humid and arid climates. This study introduced the FA algorithm as an efficient tool in NF development to estimate SMC by readily available inputs without knowing soil physical parameters.