Search Results (4)

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article

This study presents a case focused on sustainable farming practices, specifically the cultivation of tilapia (Mozambican and aureus species) in ponds with geothermal water. This research aims to optimize the hydrochemical regime of experimental ponds to enhance the growth metrics and external characteristics of tilapia breeders. The dataset encompasses the hydrochemical parameters and the fish feeding base from experimental geothermal ponds where tilapia were cultivated. Genetic algorithms (GA) were employed for hyperparameter optimization (HPO) of deep neural networks (DNN) to enhance the prediction of fish productivity in each pond under varying conditions, achieving an $R^2$ score of 0.94. This GA-driven HPO process is a robust method for optimizing aquaculture practices by accurately predicting how different pond conditions and feed bases influence the productivity of tilapia. By accurately determining these factors, the model promotes sustainable practices, improving breeding outcomes and maximizing productivity in tilapia aquaculture. This approach can also be applied to other aquaculture systems, enhancing efficiency and sustainability across various species. Full article

As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R²) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities. Full article

The quality of groundwater resources in the Pra Basin (Ghana) is threatened by ongoing river pollution from illegal mining. To date, there are very limited data and literature on the hydrochemical characteristics of the basin. For the first time, we provide regional hydrochemical data on surface water and groundwater to gain insight into the geochemical processes and quality for drinking and irrigation purposes. We collected 90 samples from surface water (rivers) and groundwater (boreholes) and analysed them for their chemical parameters. We performed a water quality assessment using conventional water quality rating indices for drinking water and irrigation. Cluster and factor analysis were performed on the hydrochemical data to learn the chemical variations in the hydrochemical data. Bivariate ion plots were used to interpret the plausible geochemical processes controlling the composition of dissolved ions in surface water and groundwater. The water quality assessment using Water Quality Index (WQI) revealed that 74% of surface water and 20% of groundwater samples are of poor drinking quality and, therefore, cannot be used for drinking purposes. For irrigation, surface water and groundwater are of good quality based on Sodium Adsorption Ratio (SAR), Wilcox diagram and United States Salinity (USSL) indices. However, Mn and Fe (total) concentrations observed in most surface water samples are above the acceptable limit for irrigation and therefore require treatment to avoid soil acidification and loss of availability of vital soil nutrients. Manganese and iron (total) are identified as the main contaminants affecting the basin’s water quality. The hierarchical cluster analysis highlights the heterogeneity in the regional hydrochemical data, which showed three distinct spatial associations based on elevation differences. Groundwater composition chemically evolves from a Ca–${\mathrm{HCO}}_3$ to a Na–${\mathrm{HCO}}_3$ and finally to a Na–Cl water type along the flow regime from the recharge to the discharge zone. The bivariate ion plot and the factor analysis underscore silicate weathering, carbonate dissolution and ion exchange as the most likely geochemical processes driving the hydrochemical evolution of the Pra Basin groundwater. Going forward, geochemical models should be implemented to elucidate the dominant reaction pathways driving the evolution of groundwater chemistry in the Pra Basin. Full article