The estimation of the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. The precise estimation of the flow coefficient is imperative in mitigating flood-related damages, administering flood alert mechanisms, and regulating water discharge. It is hard to accurately determine the flow coefficient without a good understanding of the river basin's hydrology, climate, topography, and soil characteristics. A range of methodologies have been documented in the most recent body of literature for flow coefficient modeling. The majority of these methods, however, depend on opaque techniques that lack generalizability. Therefore, this research employed three distinct methodologies-specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function, and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and Artificial Neural Network (ANN), to achieve its objectives. The Aksu River Basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation (P), temperature (T), relative humidity (Rh), wind speed (Ws), land use (LU), and soil properties (Sp) data that were tailored to the particular study region. The study analyzed the results using various performance metrics of the model such as mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values. The study's findings suggest that the SMRGT method was applied effectively in hydrological analysis to estimate the flow coefficient, contributing to more accurate flood prediction, water resource management, and flood mitigation strategies.

In geotechnical engineering, soil slopes are crucial in various civil engineering projects, including highways, embankments, dams, and excavations. Understanding the behavior of soil slopes is essential for designing stable and safe structures. Combining different soft computing (SC) models can provide more robust slope stability predictions. This paper employs two hybrid computational algorithms to make accurate slope stability predictions. In this research project, the adaptive neuro fuzzy inference system (ANFIS) model is optimized by two novel meta-heuristic optimization algorithms (MOAs): genetic algorithm (GA) and particle swarm optimization (PSO). To this end, slope inputs are taken from a literature survey consisting of 206 input datasets for the training and testing of models. Eleven statistical indices have been evaluated for assessing the performance of proposed hybrid models, along with evaluating rank analysis. ANFIS, ANFIS-GA, and ANFIS-PSO outcomes from the suggested models have R2 values of 0.6783, 0.7624, 0.7378 during training, 0.6684, 0.8143, and 0.7013 during testing. Also, the ANFIS-GA hybrid model yielded error matrices such as RMSE, MAE, and MSE with values of 0.1217, 0.0912, and 0.0148 in training and 0.12570, 0.0968, and 0.1391 in testing; in contrast, the ANFIS PSO model yielded values of 0.1264, 0.0902, 0.016 in training, and 0.1591, 0.1170, 0.1290 in testing; the ANFIS model yielded values of 0.1345, 0.1127, 0.0172 in training, and 0.1642, 0.1267, 0.1391 in testing. The regression plot was analyzed to compare the predicted value with the actual one. In the present paper, the Metropolis Hastings MCMC sampling method has been introduced to establish the relationship between the inputs, which is slope height (H), slope angle (alpha), cohesion (c), pore water pressure ratio (Ru), unit weight (Upsilon), angle of internal friction (phi), and output reliability of slopes. A sensitivity analysis was also performed to determine which variable affects the reliability of soil slope more. After that, comparing hybrid models with the ANFIS model notified the engineers and researchers that the model best predicts slope failure for extensive observations.

Earthquake damage in the twenty-first century has piqued the interest of numerous scholars and engineers working on enhancing the seismic safety of heavily populated regions. Prayagraj is one of India's fastest-growing cities, is located on the banks of the Ganga and Yamuna rivers. The river Ganga transports maximum part of alluvial soil, which is an essential factor in determining soil liquefaction potential. Some of the other factors which also affects the liquefaction potential are local site conditions, and water table. The current study focuses on liquefaction potential of soil as determined by semi-empirical approaches suggested by Modified Seed method. The developed soft computing models' assessment were compared with evaluated Liquefaction Potential which significantly matches with output of models. Therefore ANN & ANFIS models can be used for predicting Liquefaction potential of soils. The Seed's and Idriss approach are utilized for evaluating soil liquefaction potential since it has a higher estimating capacity than other standard methods. Bore log data from SPT tests done at locations were used to evaluate the liquefaction potential. For training ANN and ANFIS models, 100 datasets from thirty-three bore wells up to a depth of ten meter were gathered, while 26 datasets were retained for verifying the models. The projected findings of ANN and ANFIS models when compared to the Seed's and Idriss technique suggest that training ANN and ANFIS models were capable of accurately forecasting liquefaction potential.