The water distribution sector in Indonesia still faces challenges in detecting leaks early due to manual data checks that are time-consuming and labor-intensive. PDAM (Regional Water Company) Tirta Wijaya Cilacap, Indonesia, faces similar problems. This study aims to implement a spatial customer prediction model to detect customer water usage and support data-driven operational decision-making. K-Means clustering groups customers by consumption patterns and geographic location, achieving a Silhouette Score of 0.4473 and a Davies–Bouldin Index of 0.7658, which indicates reasonably well-separated clusters in real-world data. In addition, water consumption forecasting was carried out with Seasonal–Trend Decomposition using Loess–Long Short-Term Memory (STL–LSTM) to predict trends and seasonality of water usage for each Customer Connection ID (CCID). The forecasting performance varies across CCIDs; the best case achieves an R2 of up to 0.95, while low-performing cases are discussed to clarify conditions where STL–LSTM is less reliable. The forecasting and clustering outputs are presented through a spatial visualization (map) of water-consumption categories and model results to support identifying areas that may require closer inspection for potential leakage and waste. This research contributes to strengthening technology-based public infrastructure, in line with SDG 9: Industry, Innovation, and Infrastructure, to promote sustainable water management.

Understanding the nutrient content of soils, such as nitrogen (N), phosphorus (P), potassium (K), pH, temperature, and moisture is key to dealing with soil variation and climate uncertainty. Effective soil nutrient management can increase plant resilience to climate change as well as improve water use. In addition, soil nutrients affect the selection of suitable plant types, considering that each plant has different nutritional needs. However, the lack of integration of soil nutrient analysis in agricultural practices leads to the inefficient use of inputs, impacting crop yields and environmental sustainability. This study aims to propose a soil nutrient assessment scheme that can recommend plant types using ensemble learning and remote sensing. Remote sensing proposals support performance broadly, while ensemble learning is helpful for precision agriculture. The results of this scheme show that the nutrient assessment with remote sensing provides an opportunity to evaluate soil conditions and select suitable plants based on the extraction of N, P, K, pH, TCI, and NDTI values. Then, Ensemble Learning algorithms such as Random Forest work more dominantly compared to XGBoost, AdaBoost, and Gradient Boosting, with an accuracy level of 0.977 and a precision of 0.980 in 0.895 seconds.