Multi-objective Optimization of water resources in real time based on integration of NSGA-II and support vector machines.

One of the management strategies of water resources systems is the combination of simulation and optimization models to achieve the optimal policies of reservoir operation in the form of specific optimization. This study utilizes an integration of the NSGA-II multi-objective algorithm and WEAP simulator model so that the first objective is to maximize the reliability of providing the needs in front of the second goal, i.e., to minimize the drawdown the water table at the end of the operation time. The dam rule curve or the amount of released volume from the reservoir is optimized to supply downstream uses in these conditions. However, in certain optimizations, the optimal solutions cannot be generalized to other possible inputs to the reservoir, and if the inflow to the reservoirs changes, the obtained optimal solutions are no longer efficient and the system must be re-optimized in the form of an optimizer algorithm. Therefore, to solve this problem, a new method is extended on the basis of the combination of the support vector machine and NSGA-II algorithm for optimal real-time operation of the system. The results demonstrate that the average error rate of optimal rules derived from support vector machines is less than 2.5% compared to the output of the NSGA-II algorithm in the verification step, which indicates the efficiency of this method in predicting the optimal pattern of the dam rule curve in real time. In this structure, based on the inflow to the reservoir, the volume of water storage in the reservoir and changes in the reservoir storage (at the beginning of the month) and the downstream demands of the current month, the optimal release amount can be achieved in real time. Therefore, the developed support vector machine has the ability to provide optimal operation policies based on new data of the inflow to the dam in a way that allows us optimally manage the system in real time.

Soil-indigenous arbuscular mycorrhizal fungi and zeolite addition to soil synergistically increase grain yield and reduce cadmium uptake of bread wheat (through improved nitrogen and phosphorus nutrition and immobilization of Cd in roots).

Soil pollution with heavy metals is a major problem in industrial areas. Here, we explored whether zeolite addition to soil and indigenous arbuscular mycorrhizal fungi (AMF) can reduce cadmium (Cd) uptake from soil by bread wheat. We conducted a pot experiment, in which the effects of indigenous soil AMF, zeolite addition, and Cd spiking to soil [0, 5, 10, and 15 mg (kg soil)-1] were tested. Zeolite addition to soil spiked with 15 mg Cd kg-1 decreased the Cd uptake to grains from 11.8 to 8.3 mg kg-1 and 8.9 to 3.3 mg kg-1 in the absence and presence of indigenous AMF, respectively. Positive growth, nitrogen (N), and phosphorous (P) uptake responses to mycorrhization in Cd-spiked soils were consistently magnified by zeolite addition. Zeolite addition to soil stimulated AMF root colonization. The abundance of AMF taxa changed in response to zeolite addition to soil and soil Cd spiking as measured by quantitative polymerase chain reaction. With increasing Cd spiking, the abundance of Funneliformis increased. However, when less Cd was spiked to soil and/or when zeolite was added, the abundance of Claroideoglomus and Rhizophagus increased. This study showed that soil-indigenous AMF and addition of zeolite to soil can lower Cd uptake to the grains of bread wheat and thereby reduce Cd contamination of the globally most important staple food.