## 3 Using ML for Water Activities
The combination of different ML techniques such as multilayer perceptron, artificial neural network (MLPANN), least square support vector machine (LS-SVM) method and feedforward backpropagation neural network (FFBPANN) were most effective for analyzing water quality and predicting the performance of different water treatment processes. Therefore, hybrid ML models are more suitable for interpreting and addressing these challenges [229]. Moreover, it is equally vital to comprehend the trends in both quantity and quality of produced water (PW) within the oil and gas industry for effective management. A recent study delved into this by analyzing historical data from the New Mexico portion of the Permian Basin. ML algorithms were employed, with the random forest regression model demonstrating remarkable accuracy in predicting PW quantity. Additionally, the autoregressive integrated moving average model yielded satisfactory results in forecasting PW volume as a time series. The examination of water quality revealed intriguing insights; PW samples from the Delaware and Artesia Formations exhibited the highest and lowest average total dissolved solids concentrations, measuring 194,535 mg/L and 100,036 mg/L, respectively [230]. Furthermore, a comprehensive assessment of AI techniques applied in river streamflow forecasting revealed a dual-wave evolution. These AI models have made substantial strides in augmenting the accuracy of streamflow predictions, albeit with challenges such as overfitting and prolonged learning. The subsequent wave introduces innovative hybrid models and ensemble techniques, promising enhanced data processing efficiency and suggesting prospective research directions [231]. ML-based forecasting models have shown promise in assisting reservoir operators with releasing water during heavy rainstorms and conserving water during drought seasons. The evaluation of various models using performance metrics such as mean absolute error and R-square suggests that VARMAX performs best, indicating a seasonal component in the dataset, while ARIMA struggles to produce satisfactory results in the presence of a seasonal component. The MAE and RMSE values of both models support this argument [232]. Additionally, a study found that various machine learning models, such as Boosted Decision Tree Regression (BDTR) and Bayesian Linear Regression (BLR), performed well in predicting water levels, offering potential benefits for reservoir management. Further research should consider additional input parameters, including climate-induced rainfall changes [233]. For water resource management, power generation, and drought prevention, the accurate forecasting of water levels in reservoirs is of great importance. Hybrid metaheuristic algorithms, including ANN, ANFIS, BA, COA, and SVM, have been employed to identify factors and challenges in water level prediction from 2000 to 2020 [234]. Toimprove water management planning in drought-prone regions like the American West, the results show that RF provides the most accurate results and reduces modeling run times, enabling exploration of future climate changes and drought conditions [235]. Forecasting water levels is a critical task in disaster prevention, and while physically based models have historically been effective but computationally expensive, data-driven models like statistical ML methods and the ARIMA model offer cost-effective solutions with improved performance [236]. Recent advancements in ML algorithms have significantly enhanced the ability to forecast the complex and dynamic process of lake water level fluctuations, which are challenging to predict accurately due to their nonlinear and stochastic nature [237]. In a related context, a real-time data analysis platform uses ML to predict water consumption. It employs a web-oriented architecture for better management and monitoring of water usage. The platform collects data, handles uneven time series, and employs learning capabilities for analysis and forecasting.