Rainfall is a vital component in the rice water demand model for estimating irrigation requirements. Information on how the future patterns are likely to evolve is essential for rice-growing management. This study presents possible changes in the future monthly rainfall patterns by perturbing model parameters of a stochastic rainfall using the change factor method for the Kerian rice irrigation scheme in Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were employed to project rainfall from 2021 to 2080. The results show that the stochastic rainfall generator performed well at preserving the statistical properties between simulated and observed rainfall. Most scenarios predict the increasing trend of the mean monthly rainfall with only a few months decreasing in April and May occurring in off (dry) season. The future patterns 2051–2080 show a significant increasing trend during main (wet) season compared to the near future period (2021–2050). The projected future rainfall under SSP1-2.6 and SSP2-4.5 are higher than SSP5-8.5 from January to July, and December but lower from August to November. The projected annual rainfall will significantly increase toward 2080 during the main-season but uniform during the off-season except under SSP5-8.5, which is significantly decreasing. The output results are essential for rice farmers and water managers to manage and secure future rice irrigation water under the impact of future climate change. The projected changes in rainfall on the river basin demand further study before concluding the impact consequences for the rice sector. Article highlights: The rainfall generator performs well in simulating future rainfall based on an ensemble of five different GCMs considering three different scenarios emission (low, medium, and high) caused by greenhouse gas and radiative forcing. The future rainfall projection predicted that off (dry) season would become wet, and main (wet) season would become wetter due increase in monthly and annual rainfall. The outcomes of this paper are beneficial for rice farmers and water managers of the study area to manage their rice cultivation and water release from the reservoir schedules to avoid losses due to flood and drought.

Reference evapotranspiration (ET_o) is a crucial component in agro-meteorological processes, where accurate estimation is imperative for planning and managing irrigation practices. ET_o and rice irrigation requirements were first estimated using FAO Penman–Monteith (FAO-PM56) and the water balance model, respectively, and the obtained results were used as reference values in the machine learning algorithms. Two machine learning algorithms, named Support Vector Regression (SVR) and Random Forest (RF), were applied to predict ET_o and rice irrigation requirements using only climatic data (rainfall, temperature, relative humidity, and wind speed). The novelty of this paper is the application of machine learning techniques as an alternative to traditional methods and software solutions for estimating ET_o and irrigation demand. Emphasis is placed on the advantages of data-driven models, such as eliminating the need for internal framework factors, facilitating realistic calculations, and providing more straightforward solutions for handling multiple variables. A comparative study is conducted to evaluate the performance of SVR and RF. Comparison results indicated that the SVR model outperforms the RF model in predicting ET_o and irrigation requirements, with the minimum error observed in both the training and testing phases. All coefficient determination (R ²) values are generally higher than 0.90 except for RF during the testing phase in ET_o prediction, where the R ² value is 0.82. Alternatively, the excellent performance of SVR over RF for ET_o and irrigation requirements prediction in the training phase was also demonstrated by the mean absolute error (MAE) and root mean squared error (RMSE). The results have demonstrated that machine learning techniques, particularly the SVR model, can be confidently utilized as alternative methods for predicting ET_o and irrigation requirements, even with limited input data, with high accuracy.

Efficient composting is essential for sustainable organic waste management, yet conventional monitoring approaches are limited by single-parameter measurements and delayed response. This study presents an integrated sensor–AI framework designed to capture the interaction between thermal, chemical, and environmental factors governing composting. A distributed in-pile sensor network continuously measured temperature, moisture, and pH, while ambient parameters and gaseous emissions (O₂, CO₂, CH₄) were recorded to validate process dynamics. Statistical analyses, including correlation and regression modeling, were applied to quantify parameter interdependencies and the influence of external conditions. Results showed strong positive associations between temperature, moisture, and CO₂, and an inverse relationship with O₂, indicating active microbial respiration and accelerated decomposition. The validated sensors maintained high accuracy (±0.5 °C, ±3%, ±0.1 pH units) and supported real-time feedback control, leading to improved nutrient enrichment (notably N, P, and K) in the final compost. The framework demonstrates a transition from static measurement to intelligent, feedback-driven management, providing a scalable and reliable platform for optimizing compost quality and advancing sustainable waste-to-resource applications.