Use of Machine Learning in the Function of Sustainability of Wastewater Treatment Plants

Wastewater treatment plants (WWTP) are complex and dynamic systems whose management and sustainability can be improved by using different modelling and prediction approaches of their work. A machine learning tool for development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge to increase its sustainability and helps in its management purposes.Measured data, both in influent and effluent of the WWTP were used for modelling. For the COD model, machine learning tool Weka and algorithm for development of model trees M5P were used.Obtained model has a high descriptive power and correlation coefficient and thus can be used for prediction and modelling purposes, which can help in management and sustainability of the WWTP.Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models

Relating nutrient ratios to mucilage events in Northern Adriatic

The north western part of the northern Adriaticexhibits eutrophic to mesotrophic characteristicswith recurrent algal blooms and quiteunpredictable mucilage events. To contribute to theunderstanding of the mucilage events in thenorthern Adriatic, a machine learning algorithmfor induction of regression trees was applied to adata set comprising physical and chemicalparameters, measured at six stations on the profilefrom the Po River delta (Italy) to Rovinj on thewestern Istrian coast (Croatia). A modeldescribing the connection between the TIN/PO 4ratio, considered as a necessary factor andsometimes even a trigger for mucilage events, andthe environmental conditions in northern Adriaticwas elaborated. The model for TIN/PO 4 ratioconfirmed the assumption that the mucilage eventsare connected with the changes of this ratio in thesystem. This indicates that at certain levels of Plimitation (TIN/PO 4 signal indicate) mucilageevent frequency increases. The model also revealswhich triggers are responsible (salinity andtemperature) for the changes of the TIN/PO 4 ratioas well as their threshold values. As contrasted toto the TIN/PO 4 ratio, the mucilage events could notbe attributed to the TIN/SiO 4 ratio

Predicition of groundwater level on Grohovo landslide using ruled based regression

In order to contribute to understanding the effect of atmospheric conditions on the groundwater level fluctuations on Grohovo landslide, a machine learning tool for induction of models in form of the set of rules was applied on a dataset comprising daily atmospheric and groundwater level data measured in 2012. The atmospheric data comprises of an average daily air temperature, humidity, wind speed, pressure, total evapotranspiration, and precipitations. For the experiment independent variables i.e. atmospheric data and present groundwater level were used to model target variable i.e. predicted groundwater level for 24 and 48 hours in advance. The presented models give predictions 24 (first model) and 48 (second model) hours in advance for groundwater level fluctuations on Grohovo landslide. The first model is consisted from seven, and the second model from five rules. Both models have very high correlation coefficients of 0.99 and 0.97, respectively. From the given models, it can be concluded that the most influence on the groundwater level fluctuations have sum of daily precipitations and average daily air temperature. The obtained models are intended for use in the models for debris flow propagation on the Rječina River as a part of an Early Warning System

Performance Assessment of Wastewater Treatment Plant with Machine Learning Tools

Uređaji za pročišćavanje otpadnih voda (UPOV) s aktivnim muljem su dinamični i složeni sustavi čije se upravljanje može poboljšati primjenom različitih pristupa modeliranju i predviđanja njihova rada. U ovom radu je korišten alat strojnog učenja (modelska stabla) za izradu modela koncentracije kemijske potrošnje kisika (KPK) na izlazu pročišćene otpadne vode iz UPOV-a s aktivnim muljem. Za modeliranje su korišteni mjereni podaci na ulazu i izlazu otpadne vode iz UPOV-a. U izradi modela koncentracije KPK su korišteni programski alat Weka i algoritam za izradu modelskih stabala M5P. Model dobiven alatom strojnog učenja ima veliku opisnu moć i koeficijent korelacije te se zato može primijeniti u modeliranju koncentracije KPK. Time se u ovom radu ukazuje i na prednosti primjene alata strojnog učenja u izradi modela UPOV-a.Wastewater treatment plants (WWTPs) with activated sludge are complex and dynamic systems whose management can be improved by using different modelling and prediction approaches to their work. A machine learning tool for the development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge. The data measured in both the influent and the effluent of WWTP were used for modelling. For the COD model the machine learning tool Weka and the algorithm for the development of model trees M5P were used. The obtained model has a high descriptive power and correlation coefficient and thus can be used for modelling purposes. Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models