An effective pathway to achieve the sustainable development of resources and environmental protection is to utilize shale ceramsite (SC), which is processed from shale spoil to produce high-strength lightweight concrete (HSLWC). Furthermore, the urgent demand for better performance of HSLWC has stimulated active research on graphene oxide (GO) in strengthening mechanical properties and durability. This study was an effort to investigate the effect of different contents of GO on HSLWC manufactured from SC. For this purpose, six mixtures containing GO in the range of 0–0.08% (by weight of cement) were systematically designed to test the mechanical properties (compressive strength, flexural strength, and splitting tensile strength), durability (chloride penetration resistance, freezing–thawing resistance, and sulfate attack resistance), and microstructure. The experimental results showed that the optimum amount of 0.05% GO can maximize the compressive strength, flexural strength, and splitting tensile strength by 20.1%, 34.3%, and 24.2%, respectively, and exhibited excellent chloride penetration resistance, freezing–thawing resistance, and sulfate attack resistance. Note that when the addition of GO was relatively high, the performance improvement in HSLWC as attenuated instead. Therefore, based on the comprehensive analysis of microstructure, the optimal addition level of GO to achieve the best mechanical properties and durability of HSLWC is considered to be 0.05%. These findings can provide a new method for the use of SC in engineering.

Precise reference crop evapotranspiration (ET₀) estimation plays a key role in agricultural fields as it aids in the proper operation and management of irrigation scheduling. However, reliable ET₀ estimation poses a challenge when there is insufficient or incomplete long-term meteorological data at the East Coast Economic Region (ECER), Malaysia, where the economy is highly dependent on agricultural crop production. This study evaluated the performances of different standalone machine learning (ML) models, namely, the light gradient boosting machine (LGBM), decision forest regression (DFR), and artificial neural network (ANN) models using four different combinations of meteorological variables. The incorporation of solar radiation enhanced the accuracy of the standalone ML models, demonstrating the role of energetic factors in the evapotranspiration mechanism. Additionally, both the ANN and LGBM models showed overall satisfactory performances, and were thus recommended them as alternate models for ET₀ estimation. This was owing to their good capability in capturing the non-linearity and interaction process among the meteorological variables. The outcomes of this study will be advantageous to farmers and policymakers in determining the actual crop water demands to maximize crop productivity in data-scarce tropical regions.

Climate change is most likely to cause changes to the temporal and spatial variability of rainfall. A trend analysis to investigate the rainfall pattern can detect changes over temporal and spatial scales for a rainfall series. In this study, trend analysis using the Mann–Kendall test and Sen's slope estimator was conducted in the Kelantan River Basin, Malaysia. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test was applied to evaluate the stationarity of the rainfall series. This basin annually faces onslaughts of varying year-end flooding conditions. The trend analysis was applied for monthly, seasonal and yearly rainfall series between 1989 and 2018. The temporal analysis results showed that both increasing and decreasing trends were detected for all rainfall series. The spatial analysis results indicated that the northern region of the Kelantan River Basin showed an increasing trend, whilst the southwest region showed a decreasing trend. It was found that almost all the rainfall series were stationary except at two rainfall stations during the Inter Monsoon 1, Inter Monsoon 2 and yearly rainfall series. Results obtained from this study can be used as reference for water resources planning and climate change assessment.