Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. The deposit of SSL restricts the streamflow region, affecting aquatic life migration and finally causing a river course shift. As a result, data on suspended sediments and their fluctuations are essential for a number of authorities especially for water resources decision makers. SSL prediction is often difficult due to a number of issues such as site-specific data, site-specific models, lack of several substantial components to use in prediction, and complexity its pattern. In the past two decades, many machine learning algorithms have shown huge potential for SSL river prediction. However, these models did not provide very reliable results, which led to the conclusion that the accuracy of SSL prediction should be improved. As a result, in order to solve past concerns, this research proposes a Long Short-Term Memory (LSTM) model for SSL prediction. The proposed model was applied for SSL prediction in Johor River located in Malaysia. The study allocated data for suspended sediment load and river flow for period 2010 to 2020. In the current research, four alternative models-Multi-Layer Perceptron (MLP) neural network, Support Vector Regression (SVR), Random Forest (RF), and Long Short-term Memory (LSTM) were investigated to predict the suspended sediment load. The proposed model attained a high correlation value between predicted and actual SSL (0.97), with a minimum RMSE (148.4 ton/day and a minimum MAE (33.43 ton/day). and can thus be generalized for application in similar rivers around the world.

Enhancing the performance of data-driven models for monthly reservoir evaporation prediction

The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several parameters on the shape of the evaporation pattern. The current research presented modern model called the Coactive Neuro-Fuzzy Inference System (CANFIS). Modification for such model has been achieved for enhancing the evaporation prediction accuracy. Genetic algorithm was utilized to select the effective input combination. The efficiency of the proposed model has been compared with popular artificial intelligence models according to several statistical indicators. Two different case studies Aswan High Dam (AHD) and Timah Tasoh Dam (TTD) have been considered to explore the performance of the proposed models. It is concluded that the modified GA-CANFIS model is better than GA-ANFIS, GA-SVR, and GA-RBFNN for evaporation prediction for both case studies. GA-CANFIS attained minimum RMSE (15.22 mm month^-1 for AHD, 8.78 mm month^-1 for TTD), minimum MAE (12.48 mm month^-1 for AHD, 5.11 mm month^-1 for TTD), and maximum determination coefficient (0.98 for AHD, 0.95 for TTD).

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Heavy metal monitoring, analysis and prediction in lakes and rivers: state of the art

Several research efforts have been conducted to monitor and analyze the impact of environmental factors on the heavy metal concentrations and physicochemical properties of water bodies (lakes and rivers) in different countries worldwide. This article provides a general overview of the previous works that have been completed in monitoring and analyzing heavy metals. The intention of this review is to introduce the historical studies to distinguish and understand the previous challenges faced by researchers in analyzing heavy metal accumulation. In addition, this review introduces a survey on the importance of time increment sampling (monthly and/or seasonally) to comprehend and determine the rate of change of different parameters on a monthly and seasonal basis. Furthermore, suggestions are made for future research to achieve more understandable figures on heavy metal accumulation by considering climate conditions. Thus, the intent of the current study is the provision of reliable models for predicting future heavy metal accumulation in water bodies in different climates and pollution conditions so that water management can be achieved using intelligent proactive strategies and artificial neural network (ANN) techniques.