Journal of New Approaches in Water Engineering and Environment

Abstract This study investigates the discharge capacity of Semi-cylindrical contracted flumes installed in rectangular channels under both free-flow and submerged regimes. Two data-driven models, namely the Multilayer Perceptron Neural Network (MLPNN) and the Group Method of Data Handling (GMDH), are developed and compared. After dimensional analysis, influential dimensionless parameters were identified and used as model inputs. The MLPNN model with a single hidden layer of five neurons and tangent sigmoid activation function achieved outstanding accuracy for both free-flow (R² = 0.987, RMSE = 0.001) and submerged conditions (R² = 0.992, RMSE = 0.001). The GMDH model, while slightly less accurate (R² = 0.88 for free-flow and 0.96 for submerged), offered superior interpretability due to its transparent, self-organizing structure. Sensitivity analysis revealed that the depth-to-throat ratio (h/w) is the most influential parameter in free-flow, whereas the contraction ratio (w/B) dominates in submerged conditions. The MLPNN model is recommended for high-precision applications, whereas GMDH serves as a robust alternative when model interpretability is prioritized.

Abstract Spur dikes are widely used for bank protection and flow training in alluvial rivers, yet their stability is frequently compromised by local scour driven by coherent turbulence structures such as the horseshoe vortex system (HSV) and wake instabilities. This study experimentally evaluates a novel modular spur dike featuring regular, connected three-dimensional volumetric porosity, namely a fully porous spur dike (hereafter referred to as “Chine”), against a conventional impermeable spur dike and a hybrid three-row porous shell configuration. Experiments were conducted in a recirculating rectangular flume (B=0.60 m) under near-threshold clear-water conditions (U/Uc=0.96, Fr≈0.23, h=0.15 m) over uniform sand (d50=1.2 mm). Instantaneous three-component velocities were measured using a down-looking Nortek Vectrino II ADV at 50 Hz, while post-test bed topography was surveyed using a laser profiler (vertical accuracy ±0.5 mm). Results show that regular volumetric porosity enables a controlled bleed flow through the structure, which relieves upstream stagnation pressure, weakens near-bed downflow, and reduces the strength and near-bed influence of high-vorticity cores. Relative to the impermeable spur dike, the fully porous Chine reduced the maximum equilibrium scour depth by 23.7% (submerged) and 22.8% (non-submerged) and promoted a more stable, spatially organized downstream deposition pattern (submerged deposition-bar onset was observed 35.6% closer to the structure). The findings indicate that geometrically regular 3D porosity can function as a distributed energy dissipator that mitigates local scour risk in river-training applications.

Abstract The use of intelligent irrigation decision support systems (DSS) has become important for optimizing water consumption and enhancing productivity in agriculture. This study evaluates the "HooshAb" DSS in a 20-hectare silage maize field in Qom Province, Iran, under water scarcity. Two adjacent fields managed by the same farmer were selected as pilot sites. In the control field, irrigation followed traditional farmer management, while in the treatment field, irrigation followed DSS schedules.

Results showed that by optimizing irrigation scheduling and simulating soil moisture, the DSS reduced water consumption by approximately 6% compared to traditional methods. Furthermore, the system increased irrigation water productivity by 6.3%. Field monitoring indicated that the DSS had high accuracy in predicting soil moisture (R² = 0.75) and was effective in optimizing on-farm water management. Soil salinity monitoring revealed a relative increase during the growing season due to reduced water availability and forced deficit irrigation. Comparison of estimated versus measured salinity showed decreased accuracy toward the end of the season, highlighting the need for updating irrigation water quality data during the growing season.

Overall, the findings suggest that irrigation management DSS can contribute to soil and water sustainability and increased water productivity under climate change. Such systems enable precise irrigation scheduling, reduce unnecessary water use, and support long-term soil health. Regular updating of key input data, especially water quality parameters, is essential for maintaining system accuracy and reliability.

Abstract This study evaluates the hydraulic performance of a novel integrated pre-sedimentation and multilayer horizontal roughing filtration system for treating highly turbid surface water, developing dimensionless design relationships for process optimization. Using a laboratory-scale physical model featuring a 1 m stilling basin and a three-layer roughing filter (20, 10, and 2 mm media), 81 experimental runs were conducted. The experiments evaluated three filter lengths (260, 350, and 450 cm), three filtration velocities (0.20, 0.60, and 1.00 m h⁻¹), three operation times, and high influent turbidity (1500–2500 NTU). Input predictability was assessed using the Gamma Test. Results identified filter length as the dominant parameter, with a 260-to-450 cm increase improving turbidity removal efficiency by 9–10%, yielding maximum efficiencies exceeding 94% at 0.20 m h⁻¹. Filtration velocity exerted a secondary negative effect, while influent turbidity and operating time had limited influence. The Gamma Test confirmed a strong deterministic structure (Gamma = 0.0462, V-ratio = 0.1848). Overall, increasing the effective filter length significantly enhanced turbidity removal, demonstrating that filter length is a critical design parameter for treating highly turbid waters in resource-limited regions. However, these findings represent an experimental performance assessment of specific configurations, and absolute geometric optimization requires further investigations and techno-economic evaluation.

Abstract This study examines the treatment of gray water containing high concentrations of detergents through an integrated process combining a Sequential Batch Reactor (SBR) with UV/H₂O₂ advanced chemical oxidation. The raw gray water was characterized by relatively high pollutant levels, including 600 mg/L chemical oxygen demand (COD), 200 mg/L biological oxygen demand (BOD), 4 mg/L total phosphorus (TP), and 15 mg/L total Kjeldahl nitrogen (TKN). Biological treatment in the SBR significantly improved water quality, reducing BOD₅ to 40 mg/L and COD to 162 mg/L. The use of activated carbon, however, was found to have only a minor effect on overall treatment performance. To further enhance treatment efficiency, chemical oxidation conditions were optimized, with the best results achieved at an H₂O₂ dose of 12 mg/L and a reaction time of 3 hours under UV irradiation. Under these conditions, the combined system achieved high removal efficiencies of 90.6% for COD, 85% for BOD, 82.5% for TP, and 65% for TKN. In addition to pollutant removal, applying chemical oxidation as a pre-treatment step improved the biodegradability of organic compounds by 6%, indicating better suitability for subsequent biological treatment. The study also found that incorporating an anaerobic unit before the aeration stage enhanced phosphorus removal. However, increasing the anaerobic retention time beyond two hours did not provide any significant additional benefit. Overall, the integrated SBR and UV/H₂O₂ process proved effective for reducing pollutant loads and improving the biodegradability of gray water.

Abstract Water indices have been developed to characterize and extract the Earth's water body from space using satellite images. For detection of surface water changes, multi-temporal images are individually analyzed and compared to detect the changes. In this study, a water change index, named normalized difference water change index (NDWCI), was suggested to directly detect surface water changes between two different time images. The effectiveness of the proposed method to detect surface water change was evaluated in comparison with that of NDWI differencing and MNDWI differencing methods. Landsat imagery as one of the most widely used sources of data in remote sensing was used for this purpose. Overall accuracy and kappa coefficient were calculated to assess the accuracy of the resulting change maps. The results demonstrated that the NDWCI approach consistently outperformed both NDWI and MNDWI in terms of overall accuracy and Kappa coefficient across the two test sites. Overall, these findings indicate that NDWCI provides more robust and reliable surface water change detection than the traditional spectral indices, particularly in heterogeneous or challenging environments.

Abstract Accurate prediction of the discharge coefficient (Cd) is crucial for the hydraulic design of labyrinth weirs. While conventional vertical-sidewall geometries are widely studied, the synergistic effect of sloped sidewalls and arced planforms remains poorly understood. This study investigates and predicts Cd for arced labyrinth weirs with varying sidewall slopes using a dataset of 277 experimental runs. Five dimensionless parameters ((H0/P), (A/t), (Lc/W), (theta), (phi)), derived via dimensional analysis to capture geometric and hydraulic complexities, were used as inputs for three soft computing approaches: Support Vector Machine (SVM), Gene Expression Programming (GEP), and Artificial Neural Network (ANN). Although all models yielded satisfactory predictions, the Multilayer Perceptron (MLP) ANN model outperformed the others. The optimal MLP 5-9-1 architecture—trained using the BFGS algorithm with a logistic hidden layer and identity output layer activation—achieved training RMSE, MAE, and (R^2) of 0.022, 0.0145, and 0.8402, respectively, and validation metrics of 0.0199, 0.0145, and 0.8961. Graphical analyses confirmed strong agreement between observed and predicted values across all dataset quartiles. These findings demonstrate that the MLP framework offers a robust, reliable alternative for hydraulic design, providing a practical tool for spillway optimization where complex geometric interactions limit traditional linear regressions.

Abstract Objective:

This study evaluated the environmental impacts of the 5,000-hectare Shavur irrigation and drainage network in the central Karkheh Basin, Khuzestan Province. By utilizing both conventional and modified ICOLD matrix methods, the assessment focused on the physical, biological, and socio-economic environments during the project’s operation phase to compare the magnitude and direction of impacts across these critical components.

Results:

Findings revealed that the most significant positive outcomes occurred within the socio-economic sphere. Regarding the physical environment, the conventional method yielded 161 positive and 226 negative scores, while the modified method showed 303.57 positive and 435.91 negative scores, indicating that while the project offers physical benefits, it simultaneously poses notable adverse impacts that require careful management. In the biological environment, the conventional method produced 80 positive and 148 negative scores, compared to 111.51 positive and 286.87 negative scores via the modified approach. This suggests that vegetation, wildlife habitats, and ecological balance may face negative pressures. Conversely, the socio-economic environment demonstrated superior positive outcomes; the conventional method recorded 532 positive and 26 negative scores, whereas the modified method showed 1212 positive and 72 negative scores. These data underscore the project’s capacity to significantly boost agricultural productivity, employment, and local economic stability.

Conclusion:

Overall, the Shavur network acts as a catalyst for socio-economic growth, specifically increasing production, income, and employment. However, the project concurrently exerts negative pressures on physical and biological systems. Consequently, robust environmental management and mitigation strategies are vital to minimize ecological damage and ensure long-term environmental sustainability.

Abstract Objective “This study presents a technical soil stabilization solution by utilizing Gorgan Bay Sludge Ash (GBSA), a processed, problematic environmental by-product, for the stabilization of clayey soils with varying plasticity characteristics.”

Methods: For this purpose, a wide range of clay soils, including four samples with plasticity indices of 21, 32, 40, and 68, were selected and mixed with 0, 5, 10, 15, 20, and 25 wt.% of sludge ash. The mechanical properties of the specimens were evaluated using Atterberg limits, standard Proctor compaction, and unconfined compressive strength (UCS) tests (at curing ages of 1, 7, 28, and 56 days).

Results: “Results showed that the addition of sludge ash up to an optimum ratio of 15% led to a 5–7% increase in maximum dry unit weight and a remarkable 193–270% improvement in unconfined compressive strength (UCS) across all tested samples. Beyond this optimum level (at 20% and 25% ash), the growth in dry density and UCS slowed or declined, which is attributed to clay particle flocculation as voids were filled by the heavier ash particles. In contrast, the plasticity index decreased continuously, showing a 58% to 75% reduction at 25% ash content. Ultimately, these combined effects—increased dry density and UCS alongside reduced plasticity—significantly enhance the engineering behavior of the stabilized soil.”

Conclusions: These findings identify Gorgan Bay sludge ash as an effective additive for stabilizing problematic clay soils and offer a practical, low-cost approach for the environmentally sustainable management of this sediment waste in the region.

Abstract This experimental-intervention study was conducted at a pilot scale in 2022 to optimize the biogas production process from sugarcane vinasse specifically focusing on reducing the inhibitory effects of toxic compounds and optimizing operational parameters. The experiments were carried out based on the Biochemical Methane Potential (BMP) method at the Faculty of Agriculture, Ferdowsi University of Mashhad, Iran. The effect of six variables including temperature (37 and 55 °C), residence time (14 and 21 and 28 days), C/N ratio (1:20, 1:25, and 1:30), pH (6, 7, and 8), hydrogen peroxide concentration (2, 3 and 5 mg/L) and initial vinasse loading volume (5000 and 10000 ml) was investigated. Response surface methodology (RSM) and central composite design (CCD) were used to design the experiments. The amount of biogas produced in the process was measured based on the gas chromatography (GC) method. The results showed that the optimal conditions for biogas production were as follows: temperature 37 °C (mesophilic), time: 21 days, pH equal to 7, C/N ratio equal to one to 25, H2O2 amount equal to 3 g/L and loading volume 10000 ml. Under these conditions, the maximum biogas production efficiency reached 43.2%. Using a hydrogen peroxide concentration of 3 mg/L under these conditions helped to reduce the inhibitory effects of toxic compounds present in the vinasse and facilitated the biogas production process. These results indicate that the addition of hydrogen peroxide can be considered an effective strategy to reduce toxicity and increase the efficiency of the biogas production process.