Predictive models using “cheap and easy” field measurements: Can they fill a gap in planning, monitoring, and implementing fecal sludge management solutions?

• Published in: Water research (Oxford) Vol. 196; p. 116997
• Main Authors: Ward, Barbara J., Andriessen, Nienke, Tembo, James M., Kabika, Joel, Grau, Matt, Scheidegger, Andreas, Morgenroth, Eberhard, Strande, Linda
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.05.2021
• Subjects: Cities; fecal sludge; Feces; Filtration; image analysis; machine learning; Random forest; sanitation; Sewage; WASH; Waste Disposal, Fluid; Cities; sanitation; Random forest; fecal sludge; machine learning; image analysis; WASH
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2021.116997

•New method for image analysis of fecal sludge photos including color and texture•Solid-liquid separation performance was predicted using image analysis of photos•Simple decision tree models appear promising for citywide planning•Machine learning predictions may be sufficient for real-time process control The characteristics of fecal sludge delivered to treatment plants are highly variable. Adapting treatment process operations accordingly is challenging due to a lack of analytical capacity for characterization and monitoring at many treatment plants. Cost-efficient and simple field measurements such as photographs and probe readings could be proxies for process control parameters that normally require laboratory analysis. To investigate this, we evaluated questionnaire data, expert assessments, and simple analytical measurements for fecal sludge collected from 421 onsite containments. This data served as inputs to models of varying complexity. Random forest and linear regression models were able to predict physical-chemical characteristics including total solids (TS) and ammonium (NH4+-N) concentrations, and solid-liquid separation performance including settling efficiency and filtration time (R2 from 0.51-0.66) based on image analysis of photographs (sludge color, supernatant color, and texture) and probe readings (conductivity (EC) and pH). Supernatant color was the best predictor of settling efficiency and filtration time, EC was the best predictor of NH4+-N, and texture was the best predictor of TS. Predictive models have the potential to be applied for real-time monitoring and process control if a database of measurements is developed and models are validated in other cities. Simple decision tree models based on the single classifier of containment type can also be used to make predictions about citywide planning, where a lower degree of accuracy is required. [Display omitted]