Rainwater pumping stations located near urban centers or agricultural areas help prevent flooding by activating an appropriate number of pumps with varying capacities based on real-time rainwater inflow. However, relying solely on rule-based pump operations that monitor only basin water levels is often insufficient for effective control. In addition to maintaining a low maximum water level to prevent flooding, pump operation at rainwater stations also requires minimizing the number of pump on/off switches. Reducing pump switch frequency lowers the likelihood of mechanical failure and thus decreases maintenance costs. This paper proposes a real-time pump operation method for rainwater pumping stations using Deep Reinforcement Learning (DRL) to meet these operational requirements simultaneously, based only on currently observable information such as rainfall, inflow, storage volume, basin water level, and outflow. Simulated rainfall data with various return periods and durations were generated using the Huff method to train the model. The Storm Water Management Model (SWMM), configured to simulate the Gasan rainwater pumping station located in Geumcheon-gu, Seoul, South Korea, was used to conduct experiments. The performance of the proposed DRL model was then compared with that of the rule-based pump operation currently used at the station.

In this paper, based on the behavior of Caenorhabditis elegans (C. elegans) in response to a toxic substance, we propose a novel biological monitoring method for the detection of water contamination. Both before and after the introduction of formaldehyde into the water at the concentration of 0.1 ppm, the swimming activities of C. elegans are continuously recorded by a charge coupled device camera at the rate of four frames per second. The behavior in each of the image frames is characterized by the branch length similarity (BLS) entropy profile. The shapes quantified by the BLS entropy profiles are classified into seven shape patterns via the self-organizing map combined with the k-means clustering algorithm. Subsequently, a monitoring scheme composed of two hidden Markov models decides the water quality based on the sequence of shape patterns over a certain observation time. The performance of the proposed method is generally affected by the observation interval; yet, experimental results show an accuracy of about 83% for an observation time of five minutes. It is also observed that, by taking the distribution of individual decisions into account, the accuracy of the proposed method can be improved up to 93% and the false negative rate can be reduced to 10%.