Since the first application of Artificial Intelligence in the field of hydrology, there has been a great deal of interest in exploring aspects of future enhancements to hydrology. This is evidenced by the increasing number of relevant publications published. Random forests (RF) are supervised machine learning algorithms that have lately gained popularity in water resource applications. It has been used in a variety of water resource research domains, including discharge simulation. Random forest could be an alternate approach to physical and conceptual hydrological models for large-scale hazard assessment in various catchments due to its inexpensive setup and operation costs. Existing applications, however, are usually limited to the implementation of Breiman's original algorithm for extrapolation and categorization issues, even though several developments could be useful in handling a variety of practical challenges in the water sector. In this section, we introduce RF and its variants for working water scientists, as well as examine related concepts and techniques that have earned less attention from the water science and hydrologic communities. In doing so, we examine RF applications in water resources, including streamflow prediction, emphasize the capability of the original algorithm and its extensions, and identify the level of RF exploitation in a variety of applications.

In order to provide an effective tool for the simulation of wastewater treatment plant performance and control, a reliable model is essential. In the present study, two different artificial intelligence (AI) models; Adaptive Neuro-Fuzzy inference system (ANFIS), and a classical multi-linear regression analysis (MLR) were applied for predicting the performance of Abuja wastewater treatment plant (AWWTP), in terms of Conductivity, pH, Iron content, BOD, COD, TSS and TDS. The daily data were obtained from Abuja Wastewater treatment plant, for this purpose, single and ensemble models were employed to compare and improve the prediction performance of the plant. The obtained result of single models proved that, MLR model provides an effective analysis in comparison to the other single model. The result showed that, conductivity influences the performance and efficiency of the water treatment plant by an increased efficiency performance of AI modelling up to 99.6% testing phase and 6.8% Error value of same phase. This shows that MLR model was more robust and reliable method for predicting the Abuja WWTP performance.

The development of computer models for water quality index forecasting has been a leading research topic worldwide which has been considerably recognized over the last two decades; the balance between efficient water quality requires a good water management technique. The balance is said to be achieved through various procedures many of which require the application of computer-aided forecasting tools. In this paper, a decade research review on the water quality index in the field of artificial intelligence was carried out with the aim to present the most viable or most suitable methods and models to be adopted for future researchers in the field of water quality. The review incorporates the developed models such as ANN, ANFIS, SVM, other regression or time-series, and other soft computing models. This research shows that the study focused on a decade review of the methods and models, and also, there is room for long-term forecasts. It also shows that there is no single AI model that outperforms all the remaining AI models but It is necessary to evaluate the strength of each model combination for each region thus to know what type of method or model that works best for the country or region. The use of AI has grown significantly in recent decades however there is enough room for researchers to duel in and improve in the field of water quality index.