Prediction of flood using optimized neural network with Gray wolf algorithm (Maroon River case study)

Floods, as one of the most frequent natural hazards, cause irreparable damage to infrastructure and agriculture, and housing every year. In order to avoid financial and human losses, the importance of flood forecasting seems inevitable. Considering that floods are caused by many natural and anthropogenic factors and also have limitations such as lack of complete information. In this study, artificial neural networks have been used as an efficient method for flood prediction. The neural network inputs include the Dubai River and the Eshel River, this data was collected over 8 Years from the Maroon River. The network used is a multilayer perceptron, also the neural network weights are optimized by the Gray wolf algorithm and the results are compared with other common methods. Analysis of the output results shows that the neural network with the Gray Wolf algorithm has better results than neural network and Genetic algorithms and the error of this method is 0.53%, which indicates high accuracy and precision for flood prediction compared to other evolutionary algorithms. This method is used to obtain the best amount of data for testing and training. As the results, the best rate is 80% for training and 20% for testing. Obtained results show the neural network error squares with 80% of the training data and 20% of the test data.

Hydrochemistry and water quality assessment of the Maroon River in Behbahan area, SW Iran

Assessing water quality is important in optimizing water usage. In this study, six water samples were taken from points along the Maroon River in order to evaluate its quality. The concentrations of Cl−, SO42−, HCO3−, CO32−, NO3−, K+, Mg2+, Na+, and Ca2+, as well as pH, electrical conductivity (EC) and total dissolved solids (TDS) were measured. The results were compared with the WHO (World Health Organization) drinking water guidelines and the FAO (Food and Agricultural Organization of United Nations) standard for irrigation water. The values of EC, TDS, Ca2+, Cl− and SO42− in two samples, and that of Mg2+ in one sample from downstream exceed WHO recommended limits. The K+, Na+ and NO3− concentrations are below the WHO limits. The dominant water facies is bicarbonate-calcium (HCO3-Ca). A Schoeller diagram shows that the water samples are of ‘Good’ and ‘Intermediate’ classes, while a Wilcox diagram reveals that the water is in classes C2S1, C3S3 and C3S2 – i.e., ‘Good’ or ‘Intermediate’ – for agricultural purposes. The water quality indices %Na, sodium adsorption ratio (SAR), residual sodium carbonate (RSC), permeability index (PI), magnesium adsorption ratio (MAR) and Kelly's ratio (KR), also indicate that the water is suitable for irrigation. In terms of EC, the water samples are of ‘Good’ and ‘Permissible’ quality. Geochemical investigations show that the water chemistry is influenced by evaporation, dissolution of evaporitic minerals, ion exchange and human activities.