Pekalongan City is located on the northern coast of Java Island, where climate crises, floods, and tidal waves pose significant threats exacerbated by rising sea levels and land subsidence. This study aims to mitigate of flood future losses and emphasizes its potential as input for Indonesian regulators to enhance current flood hazard classifications. This study conducts a comprehensive flood risk analysis, emphasizing a detailed review of flood hazard classification quantitatively with flood hazard mapping. The results showed flood hazard mapping, comparing NDMA, MPW, and proposed classifications, indicates a tendency for MPW to overestimate hazards, particularly categorizing depths of 0.5 m and above as “High”. The flood depth according to MPW with land subsidence produces a flood inundation area of 14,145 Ha. This shows that land subsidence recorded in risk mapping can increase the overall hazard level. This study proposed flood depth and velocity classification, balancing NDMA’s underestimation and MPW’s overestimation, offering a more comprehensive approach.

The combination of permeable and impermeable spur dike was proposed to reduce severe local scour and to create diverse reverbed configuration. The purpose is to find out an optimum distance between pile-group and solid spur dike for reducing the local scour. We investigated the flow structure and bed deformation around a single spur dike downstream of a pile-group in an open channel. A series of experiments were conducted under emerged flow and clear-water scour conditions. In the cases with an upstream pile-group, the local scour was faded with a noticeable decrease in the depth and amount of bed erosion. The scour depth and volume were decreased as the distance increased from zero to four times the spur dike length. In the cases with a pile-group, flow near the spur dike became lower relative to the single solid spur-dike case. The flow structures around the combination of the pile-group and the spur dike were well simulated by a 2D numerical model and the reduction of the bed shear stress near the spur dike by the pile-group was demonstrated.

Pressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations () is critical for hydraulic engineers. This study proposed predictive soft computing models to estimate on sloping channels. Therefore, three robust soft computing methods, including extreme learning machine (ELM), group method data of handling (GMDH), and M5 model tree (M5MT), were used to estimate . The results revealed that ELM was more accurate than GMDH and M5MT methods when comparing statistical indices, including correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), index agreement (I_a), and BIAS values. The performance of ELM was found to be more accurate (CC = 0.9183, RMSE = 0.0067, MAE = 0.0051, SI = 11.88%, Ia = 0.9569) when compared with the results of GMDH (CC = 0.8818, RMSE = 0.0078, MAE = 0.0058, SI = 13.89%, Ia = 0.9361) and M5MT (CC = 0.6883, RMSE = 0.0120, MAE = 0.0090, SI = 21.28%, Ia = 0.7905) in the testing stage. In addition, the BIAS values revealed that ELM slightly overestimated the values of , especially at the peak point compared with GMDH and M5MT results. Overall, the suggested soft computing techniques worked well for predicting pressure fluctuation changes in the hydraulic jump.