A geotechnical perspective on soil-termite interaction: Role of termites in unsaturated soil properties

The soil-insect interaction has gathered significant attention in the recent years due to its contribution to bio-cementation. Termites, as a group of cellulose-eating insects, alter physical (texture) and chemical (chemical composition) properties of soil. Conversely, physico-chemical properties of soil also influence termite activities. It is vital to understand the soil-termite interaction and their influence on hydraulic properties and shear strength of soil, which are related to a series of geotechnical engineering problems such as ground water recharge, runoff, erosion and stability of slopes. In this study, an attempt has been made to review the latest developments and research gaps in our understanding of soil-termite interaction within the context of geo-environmental engineering. The hydraulic properties and shear strength of termite modified soil were discussed with respect to soil texture, density and physico-chemical composition. The incorporation of hysteresis effect of soil water characteristic curve, and spatio-temporal variations of hydraulic conductivity and shear strength of termite modified soil is proposed to be considered in geotechnical engineering design and construction. Finally, the challenges and future trends in this research area are presented. The expertise from both geotechnical engineering and entomology is needed to plan future research with an aim to promote use of termites as maintenance engineers in geotechnical infrastructure.

Hybridization of long short-term memory with Sparrow Search Optimization model for water quality index prediction

Water quality (WQ) analysis is a critical stage in water resource management and should be handled immediately in order to control pollutants that could have a negative influence on the ecosystem. The dramatic increase in population, the use of fertilizers and pesticides, and the industrial revolution have resulted in severe effects on the WQ environment. As a result, the prediction of WQ greatly helped to monitor water pollution. Accurate prediction of WQ is the foundation of managing water environments and is of high importance for protecting water environment. WQ data presents in the form of multi-variate time-sequence dataset. It is clear that the accuracy of predicting WQ will be enhanced when the multi-variate relation and time sequence dataset of WQ are fully utilized. This article presents the Water Quality Prediction utilising Sparrow Search Optimization with Hybrid Long Short-Term Memory (WQP-SSHLSTM) model. The presented WQP-SSHLSTM model intends to examine the data and classify WQ into distinct classes. To achieve this, the presented WQP-SSHLSTM model undergoes data scaling process to scale the input data into uniform format. Followed by, a hybrid long short-term memory-deep belief network (LSTM-DBN) technique is employed for the recognition and classification of WQ. Moreover, Sparrow search optimization algorithm (SSOA) is utilized as a hyperparameter optimizer of the proposed DBN-LSTM model. For demonstrating the enhanced outcomes of the presented WQP-SSHLSTM model, a sequence of experiments has been performed and the outcomes are reviewed under distinct prospects. The WQP-SSHLSTM model has achieved 99.84 percent accuracy, which is the maximum attainable. The simulation outcomes ensured the enhanced outcomes of the WQP-SSHLSTM model on recent methods.