Water pollution escalates with rising waste discharge in river systems, as the rivers\' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA\'s Pareto front is superior to NSGA-II, which highlights the MOCOA\'s effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size.

In this research, to predict landslide susceptibility mapping (LSM), we have studied and optimized an artificial neural network (ANN) by utilizing the backtracking search algorithm (BSA) as well as the Cuckoo optimization algorithm (COA). Multiple research studies have shown that ANN-based techniques can be used to figure out the LSM. Still, ANN computing models have big problems, like slow system learning and getting stuck in their local minimums. Optimization strategies may improve ANN performance results. Existing uses of the BSA and COA models in ANN training have not been used to map landslides, nor have the best ways to set up networks or other factors that affect this problem been examined. Consequently, the present research focuses on predicting landslide susceptibility for hazardous mapping using hybrid BSA and COA-based ANN algorithms (BSA-MLP and COA). A large data set was provided from an area in the province of Kurdistan, west of Iran, to provide training and testing datasets for the algorithms. All of the BSA and COA algorithms\' parameters and weights, for instance, were fine-tuned to make the utmost accurate maps of landslide risk. The input dataset consists of elevation, slope angle, slope orientation, NDVI, fault tolerance, profile curvature, plan curvature, distance to the river, rainfall, far from the road, SPI, STI, TRI, TWI, land use, and geology; the output is landslide susceptibility value. In the testing phase, the AUC rose significantly from 0.701 to 0.864 for BSA-MLP and 0.738 to 0.822 for COA-MLP after using the abovementioned techniques. We have used the area under the curve (AUC) to evaluate how well the probabilistic models worked. In addition, the computed AUCs for the BSA-MLP available databases and the actual AUCs were 0.864, 0.857, 0.833, 0.778, 0.777, 0.769, 0.763, 0.758, 0.727, and 0.701 and 0.822, 0.808, 0.807, 0.805, 0.804, 0.777, and 0.769 for the COA-MLP combination. The integrated models can produce beneficial results for this area of research. The results suggest that the BSA-ANN model is better than the COA-ANN in optimizing an artificial neural network model\'s structure and computational parameters. The collected landslide susceptibility maps are significant for figuring out how dangerous landslides are in the studied area.