Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models

Rainfall-induced landslides are a major hazard in the Three Gorges Reservoir area (TGRA) of China, encompassing 19 districts and counties with extensive coverage and significant spatial variation in terrain. This study introduces the Gradient Boosting Decision Tree (GBDT) model, implemented on the Google Earth Engine (GEE) cloud platform, to dynamically assess landslide risks within the TGRA. Utilizing the GBDT model for landslide susceptibility analysis, the results show high accuracy with a prediction precision of 86.2% and a recall rate of 95.7%. Furthermore, leveraging GEE’s powerful computational capabilities and real-time updated rainfall data, we dynamically mapped landslide hazards across the TGRA. The integration of the GBDT with GEE enabled near-real-time processing of remote sensing and meteorological radar data from the significant “8–31” 2014 rainstorm event, achieving dynamic and accurate hazard assessments. This study provides a scalable solution applicable globally to similar regions, making a significant contribution to the field of geohazard analysis by improving real-time landslide hazard assessment and mitigation strategies.

Section: Discussionmentioning

confidence: 99%

Dynamic Hazard Assessment of Rainfall-Induced Landslides Using Gradient Boosting Decision Tree with Google Earth Engine in Three Gorges Reservoir Area, China

Yang,

Niu,

Song

et al. 2024

“…Achite et al [27] introduced a hybrid model known as the M5-Gorilla Troops Optimizer (GTO), built upon a blend of the M5 and GTO algorithms. This research employed nine diverse parameters including raw water production (RWP), water turbidity, conductivity, TDS, salinity, pH, water temperature (WT), SM, and O2 as inputs for CD modeling.…”

Section: Introductionmentioning

confidence: 99%

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

Abba,

Usman,

Abdulazeez

et al. 2023

The need for reliable, state-of-the-art environmental investigations and pioneering approaches to address pressing ecological dilemmas and to nurture the sustainable development goals (SDGs) cannot be overstated. With the power to revolutionize desalination processes, artificial intelligence (AI) models hold the potential to address global water scarcity challenges and contribute to a more sustainable and resilient future. The realm of desalination has exhibited a mounting inclination toward modeling the efficacy of the hybrid nanofiltration/reverse osmosis (NF–RO) process. In this research, the performance of NF–RO based on permeate conductivity was developed using deep learning long short-term memory (LSTM) integrated with an optimized metaheuristic crow search algorithm (CSA) (LSTM-CSA). Before model development, an uncertainty Monte Carlo simulation was adopted to evaluate the uncertainty attributed to the prediction. The results based on several performance statistical criteria (root mean square error (RMSE) and mean absolute error (MAE)) demonstrated the reliability of both LSTM (RMSE = 0.1971, MAE = 0.2022) and the LSTM-CSA (RMSE = 0.1890, MAE = 0.1420), with the latter achieving the highest accuracy. The accuracy was also evaluated using new 2D graphical visualization, including a cumulative distribution function (CDF) and fan plot to justify the other evaluation indicators such as standard deviation and determination coefficients. The outcomes proved that AI could optimize energy usage, identify energy-saving opportunities, and suggest more sustainable operating strategies. Additionally, AI can aid in developing advanced brine treatment techniques, facilitating the extraction of valuable resources from the brine, thus minimizing waste and maximizing resource utilization.

“…Using deep learning methods for water quality prediction can solve the problem of difficult application of comprehensive water quality models, as deep learning methods can effectively establish relationships between water quality parameters without complex boundaries and initial conditions [3]. Deep learning methods have been widely used in engineering problems [15][16][17]. In recent years, artificial intelligence models, such as artificial neural networks (ANNs), have gradually been applied to hydrological process analysis [18][19][20] and water quality prediction [7,21,22].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China

Luo

Zhang

Dong

et al. 2023

Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and difficult to apply. However, as water quality time series are generally multiperiod hybrid time series, which have strongly nonlinear and nonstationary characteristics, the prediction accuracy of LSTM for water quality is not high. The ensemble empirical mode decomposition (EEMD) method can decompose the multiperiod hybrid water quality time series into several simpler single-period components. To improve the accuracy of surface water quality prediction, a water quality prediction model based on EEMD–LSTM was developed in this paper. The water quality time series was first decomposed into several intrinsic mode function components and one residual item, and then these components were used as the input of LSTM to predict water quality. The model was trained and validated using four water quality parameters (NH3-N, pH, DO, CODMn) collected from the Xiaofu River and compared with the results of a single LSTM. During the validation period, the R2 values when using LSTM for NH3-N, pH, DO and CODMn were 0.567, 0.657, 0.817 and 0.693, respectively, and the R2 values when using EEMD–LSTM for NH3-N, pH, DO and CODMn were 0.924, 0.965, 0.961 and 0.936, respectively. The results show that the developed model outperforms the single LSTM model in various evaluation indicators and greatly improves the model performance in terms of the hysteresis problem. The EEMD–LSTM model has high prediction accuracy and strong generalization ability, and further development may be valuable.