Predictive Analytics for Smart Water Management in Developing Regions

Water availability and management is an important problem plaguing many developing and under-developed countries. Many factors including geographic, political, management, and environmental factors affect the availability of water in these regions. In this paper, we develop an ensemble-learning based predictive-analytics framework for smart water management to predict: i) water pump operation status (e.g., functional, non functional), ii) water quality, and iii) quantity. In the predictive-analytics framework, we first perform feature engineering to select relevant features, use them to develop the XGBoost and Random Forest ensemble learning models, and then perform extensive feature analysis to identify the most predictive features, for each prediction problem mentioned above. We evaluate our framework on two publicly available smart water management datasets pertaining to Tanzania and Nigeria and show that our proposed models outperform several baseline approaches, including logistic regression, SVMs, and multi-layer perceptrons in terms of precision, recall and F1 score. We also demonstrate that our models are able to achieve a superior prediction performance for predicting water pump operation status for different water extraction methods. We conduct a detailed feature analysis to investigate the importance of the various feature groups (e.g., geographic, management) on the performance of the models for predicting water pump operation status, water quality and quantity. We then perform a fine-grained feature analysis to identify how individual features, not just feature groups, impact performance. We identify that among individual features, location (x, y, z coordinates) has the maximum impact on performance. Our analysis is helpful in understanding the types of data that should be collected in future for accurately predicting the different water problems.