非生育期春灌灌水量对土壤盐分变化的影响

通过田间小区试验,分析3个不同滴灌棉田春灌灌水量1350、1800和2250 m3/hm2对盐分变化的影响。试验表明:在相同灌水量下,宽行、窄行和膜间3个不同行距的脱盐效果,宽行最好;3个灌水量下,1800 m3/hm2的灌水量对盐分淋洗效果较好。此结果可为当地春灌提供指导,对制定灌溉制度有一定的借鉴作用。     

Assessment of AquaCrop Model in Simulating Sugar Beet Canopy Cover,Biomass and Root Yield under Different Irrigation and Field Management Practices in Semi-Arid Regions of Pakistan

The AquaCrop model was analyzed for simulating sugar beet crop production under four irrigation regimes, three mulching conditions and three furrow irrigation systems in semi-arid region of Pakistan. Irrigation regimes were full irrigation (FI), 20% deficit irrigation (DI₂₀), 40% deficit irrigation (DI₄₀) and 60% deficit irrigation (DI₆₀). The mulching practices were No-mulch (NM), black film mulch (BFM) and straw mulch (SM). The furrow irrigation systems were conventional ridge-furrow (CRF) system, medium raised-bed (MRB) system and wide raised-bed (WRB) system. The model was calibrated and validated using the independent data sets of full irrigation and deficit irrigation regimes collected during 2011–12 cropping season. The model performance was evaluated by using different statistical indicators such as Root Mean Square Error (RMSE), index of agreement (d_index), and Nash–Sutcliffe Efficiency (NSE). These indicators showed that the model fairly simulated sugar beet canopy cover for all treatments with 3.00 ≤ RMSE ≤ 16.89, 0.84 ≤ d_index ≤ 0.97, and 0.76 ≤ NSE ≤ 0.99. For biomass and root yield, the model performance was excellent under all full irrigation (FI) and mild deficit irrigation (DI₂₀) treatments with RMSE ranged between 0.07 and 1.17, d_index between 0.48 and 0.84, and NSE between 0.42 and 0.86, respectively. However the low values of d_index (0.10 and 0.13) and NSE (?69.32 and ?30.63) showed that the model overestimated both the biomass and root yield when 20% deficit irrigation was applied without mulch in WRB system. The model also over estimated the yield and biomass when 40% deficit irrigation was applied in CRF system. The highest overestimation (d_index: 0.10 to 0.11; NSE: ?50.92 to ?70.55) was observed when highest stress level (DI₆₀) was applied in the presence of BFM in CRF system. Based on the model’s overall performance, the AquaCrop application is recommended for developing efficient farm water management strategies in the semi-arid regions.     

Investigation of stage-discharge model performance for streamflow estimating: A case study of the Gono River,Japan

In water resource studies, long-term measurements of river streamflow are essential. They allow us to observe trends and natural cycles and are prerequisites for hydraulic and hydrology models. This paper presents a new application of the stage-discharge rating curve model introduced by Maghrebi et al. (2016) to estimate continuous streamflow along the Gono River, Japan. The proposed method, named single stage-discharge (SSD) method, needs only one observed data to estimate the continuous streamflow. However, other similar methods require more than one observational data to fit the curve. The results of the discharge estimation by the SSD are compared with the improved fluvial acoustic tomography system (FATS), conventional rating curve (RC), and flow-area rating curve (FARC). Some statistical indicators, such as the coefficient of determination (R²), root mean square error (RMSE), percent bias (PBAIS), mean absolute error (MAE), and Kling-Gupta efficiency (KGE), are used to assess the performance of the proposed model. ADCP data are used as a benchmark for comparing four studied models. As a result of the comparison, the SSD method outperformed of FATS method. Also, the three studied RC methods were highly accurate at estimating streamflow if all observed data were used in calibration. However, if the observed data in calibration was reduced, the SSD method by R² = 0.99, RMSE = 2.83 (m³/s), PBIAS = 0.715(%), MAE = 2.30 (m³/s), and KGE = 0.972 showed the best performance compared to other methods. It can be summarized that the SSD method is the feasible method in the data-scarce region and delivers a strong potential for streamflow estimation.     

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET₀). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET₀ was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET_0.     

膜下滴灌不同灌水定额及水氮耦合对打瓜耗水规律的影响

为研究不同灌水定额和不同施氮量对北疆打瓜耗水规律的影响,2016-2017年在新疆阿勒泰地区灌溉试验站陆续开展2 a大田打瓜灌水试验,讨论连续2 a不同灌水定额及水氮互作下打瓜耗水规律。2016年试验设置5个不同的灌水定额分别为300、375、450、525、600 m~3/hm~2。2017年设置3个不同灌水定额分别为300、450、600m~3/hm~2,3个不同施氮量分别为0、138、276 kg/hm~2,共9个处理。研究结果表明:不同灌水定额和水氮互作下打瓜旬均耗水量变化规律均呈先增大后减小变化规律;耗水量、耗水强度和果实膨大期的耗水模数随着灌水定额增加而呈"增大-平缓"态势;打瓜作物系数随灌水定额增加而增大,且在0. 360～0. 431间变化,其中600 m~3/hm~2灌水定额耗水量、耗水强度和作物系数最大;合适的水氮供给量有利于打瓜耗水模数、作物系数、耗水量和耗水强度增大,作物系数在0. 317～0. 445间变化;与其他生育阶段相比,不同灌水定额和水氮互作均在果实膨大期对打瓜耗水量、耗水强度和作物系数的影响最大。研究可为理解大田打瓜的耗水特性和当地打瓜节水高效生产提供依据。     

河套灌区耕地-荒地-海子间水盐运移规律及平衡分析

内蒙古河套灌区引水量逐年减少,输入灌区的盐分无法有效排出,灌区内部盐分迁移规律发生了较大变化。针对此现状,通过2年现场观测试验,开展了耕地-荒地-海子间水力联系及水盐动态研究。采用土壤水动力学与溶质动力学方法,对耕地—荒地—海子系统分别构建水量和盐量平衡模型,进而揭示了耕地—荒地—海子系统间水分和盐分运移关系。结果显示:在作物生育期,土壤非饱和带及地下储水量?S分别平均减少了35.05、138.2和195.7 mm,耕地、荒地和海子均处于水分消耗状态。基于Surfer软件Grid Vector Map与耕地—荒地—海子系统水量和盐量平衡模型,发现在2017年和2018年作物生育期,耕地地下水向荒地的迁移率平均值分别为78.75%和79%;荒地地下水向海子的迁移率平均值分别为44.3%和46.3%。耕地腾发量ET平均值比荒地和海子边界的分别高32%和29.15%,海子亏水631.2～706.3 mm,如果没有水分补给,海子将会面临干涸危险;在灌溉条件下,耕地地下水盐分平均增加861.45 kg/(hm~2/a),耕地地下水迁移给荒地的平均盐量为3231.90 kg/(hm~2/a),荒地地下水迁移给海子的平均盐量为3139.7 kg/(hm~2/a)。研究结果为灌区水盐调控提供了基本依据。     

基于熵权-TOPSIS的滴灌春玉米灌水定额研究

为准确得到适宜于北疆地区滴灌春玉米的灌水定额,设置了W₁(225 m³/hm²)、W₂(300 m³/hm²)、W₃(375 m³/hm²)、W₄(450 m³/hm²)、W₅(525 m³/hm²)、W₆(600 m³/hm²) 6个水平的灌水定额,研究灌水定额对春玉米生长发育和产量的影响。选取部分生长指标、产量指标、节水指标作为评价指标,结合耗水规律和指标显著性进行分析,并采用熵权-TOPSIS综合评价法,优选适宜于北疆地区滴灌春玉米的灌水定额。结果表明：滴灌春玉米的关键生育期为抽雄散粉期,该生育期最优灌水定额为525 m³/hm²;耗水量和耗水强度...     

宁夏半干旱区玉米滴灌灌溉制度试验研究

为了促进宁夏半干旱区节水灌溉技术的推广应用,本文针对宁夏扬黄灌区规模推广玉米大田滴灌缺乏相关滴灌灌溉制度的突出问题,采用大田小区对比试验与示范区技术应用监测相结合的方法,开展了玉米裸地滴灌灌溉制度试验研究。在综合分析玉米生育期灌溉定额、土壤含水率变化、耗水量、作物产量、水分生产效率的基础上,研究提出了玉米生育期滴灌11~12次、灌水定额300~450 m3/hm~2,灌溉定额4 200~4 425 m3/hm~2的滴灌灌溉制度。研究结果表明:玉米大田滴灌目标产量15 000 kg/hm~2,水分生产效率达到3.0 kg/m3,玉米滴灌较大田畦灌可提高经济效益3 735元/hm~2,同时节水1 800 m3/hm~2、节水30%以上。     

Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

Water Savings Through Improved Irrigation Techniques: Basin-Scale Quantification in Semi-Arid Environments

In many semi-arid and arid regions of the world, water saving strategies need to be implemented in the agricultural sector in order to increase the resilience to water scarcity. We investigate basin-scale hydrological impacts of possible irrigation technique improvements, considering extensive cotton fields in the Aral Sea drainage basin (ASDB), Central Asia. We use a distributed hydrologic model that combines basin-scale, calibrated discharge and evapotranspiration quantifications with experimental results of (on-farm) water application needs for different irrigation techniques. This allows for quantification of how return flows contribute to river discharge through coupled groundwater-surface water-systems at the basin scale, under different regional climatic conditions. Results show that an implementation of improved irrigation techniques can yield water savings that increase the discharge to the Aral Sea by between 1 and 6 km³/year. Such water savings could contribute to mitigation of the acute water scarcity in the lower ASDB. The basin-scale water savings are about 60% lower than corresponding on-farm reductions in irrigation water application, since water is re-used and, hence, return flows decrease when less water is applied. Spatial analysis of regional differences in climatic conditions shows that implementation of more efficient irrigation systems would result in much larger (up to a factor 4) water savings in the more arid downstream regions than in the colder, upstream mountainous regions.     

Rainwater Management for Smallholder Irrigation and it’s Impact on Crop Yields in Eastern India

A tank cum open dug well system suitable for plateau region of eastern India has been developed for providing reliable irrigation to croplands. The system comprises of a series of tanks with open dug wells in the recharge zone of the tank that reharvest back the seepage water. Thus, the rainwater remaining in the tank as well as partial seeped water is used for providing round the year full irrigation. This system was evaluated in field in Keonjhar district of Orissa of eastern India with six tanks and five wells in two drainage lines. The total command area of the system of six tanks and five wells in both drainage lines is 23 ha and the total irrigation potential is 44.5 ha. The total cost of the system is US $19,180 making the cost of irrigation resource creation as US $426 per ha which is much less than about $2,220 per ha for major and medium irrigation projects in the last decade of 20th century. The system increased the rice yields from 1.92 t ha^− 1 to a range of 2.25 to 3.8 t ha^− 1 depending upon the package of practices or the amount of inputs. The farmers went for crops in post-monsoon and summer season and the cropping intensity rose to 112% in the first year, 126% in the second year and 132% in the third year. The internal rate of return from the system was 13.4% at the present level of utilization, which is about 2.4% more than the prime-lending rate of Indian banks, and 3.4% more than the lending rate for agricultural purposes.     

Optimal sampling designs for estimating salmonid run abundances from video recordings at a fishway on the Bois Brule River,Wisconsin, a tributary to Lake Superior

Using a simulation program and video census data (2004–2018) from the Bois Brule River fishway, Wisconsin, USA, we compared alternative sampling designs to estimate spawning run abundances of steelhead, coho salmon, Chinook salmon, and brown trout. We evaluated two types of two-stage sampling designs, comprising varying numbers of days sampled within a year (1st stage samples) and varying numbers of hours sampled within a day (2nd stage samples). While days were sampled using stratified random sampling under both types of sampling designs, hours were sampled using uniform (1/24) or non-uniform (proportional to hourly runs) selection probabilities under the first and second types of sampling designs, respectively. Number of days sampled within a year, comprising three strata, varied from 30 to 200 days in 10-day increments, and number of hours sampled within a day varied from 2 to 24 h in 2-hour increments. Spawning run sizes of the salmonids could be estimated with a relative root mean square error (RMSE) of less than 10% on average by employing a two-stage sampling design with samples of 100 days·yr⁻¹ and 8 hrs·day⁻¹, i.e., 800 hrs·yr⁻¹; by contrast, full census involved reviewing 250 days·yr⁻¹ and 24 hrs·day⁻¹, i.e., 6000 hrs·yr⁻¹, of video. Sampling more days (>100) resulted in greater reductions in estimation error than sampling more hours (>8). Non-uniform (vs. uniform) selection probabilities for hours sampled slightly reduced error of estimates. Our results underscore that optimal sampling designs could ensure a considerable reduction in survey resources while maintaining relatively low error in estimation of salmonid abundances.     

A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating

Streamflow estimation is highly significant for water resource management. In this work, we improve the accuracy and stability of streamflow estimation through a novel hybrid decompose-ensemble model that employs variational mode decomposition (VMD) and back-propagation neural networks (BPNN). First, the latest decomposition algorithm, namely, VMD, was used to extract multiscale features that were subsequently learned and ensembled by the BPNN model to obtain the final estimate streamflow results. The historical daily streamflow series of Laoyukou and Wushan hydrological stations in China were analysed by VMD-BPNN, by a single GBRT and BPNN model, ensemble empirical mode decomposition (EEMD) models. The results confirmed that the VMD outperformed a single-estimation model without any decomposition and EEMD-based models; moreover, ensemble estimations using the BPNN model development technique were consistently better than a general summation method. The VMD-BPNN model’s estimation performance was superior to that of five other models at the Wushan station (GBRT, BPNN, EEMD-BPNN-SUM, VMD-BPNN-SUM, and EEMD-BPNN) using evaluation criteria of the root-mean-square error (RMSE?=?2.62 m³/s), the Nash–Sutcliffe efficiency coefficient (NSE?=?0. 9792) and the mean absolute error (MAE?=?1.38 m³/s). The proposed model also had a better performance in estimating higher-magnitude flows with a low criterion for MAE. Therefore, the hybrid VMD-BPNN model could be applied as a promising approach for short-term streamflow estimating.

     

Quantitative Assessment of Agricultural Practices on Farmland Evapotranspiration Using EddyCovariance Method and Numerical Modelling

While low cost eddy covariance (EC) techniques based on the open-path laser analyzers have been widely used, they are not very accurate and are restricted to use in non-rainy days. As an alternative, and relevant to promoting precision agriculture where water availability is proving key, the application of the EC technique based on closed-path QCLAS-EC Analyzer is described, in a study of cabbage farmland evapotranspiration (ET). This study uses the advantages of the closed-path EC method to quantitatively assess the impact of agricultural activities on farmland ET and compared with RZWQM2 (Root Zone Water Quality Model) model. The cumulative ET is shown to have increased by 1.5–3.6 mm over ten-days after planting, and decreased by 3.5–8.1 mm over 10 days, following harvesting. While irrigation contributed to ET, the cumulative ten-day ET increase is between 0.6–2.3 mm which is significantly lower than the effects due to planting and harvesting. The RZWQM2 model was used to quantify the effects of four agricultural practices. Simulation of ET attained R² and RMSE as 0.79 and 0.013 mm/d, respectively. In addition, the RZWQM2 model successfully simulated groundwater levels, Leaf area index, and crop height (R² values of 0.71, 0.98 and 0.97, respectively). The RZWQM2 model simulates the effects of planting, harvesting, and irrigation on ET, indicating the same directional changes of magnitude with measured data. The results provide a comprehensive and direct study based on the closed-path EC method and RZWQM2 model to assess the impact of multiple agricultural activities on farmland ET and to improve precision agriculture.

     

Development of a Glacio-hydrological Model for Discharge and Mass Balance Reconstruction

The reconstruction of glacio-hydrological records for the data deficient Himalayan catchments is needed in order to study the past and future water availability. The study provides outcomes of a glacio-hydrological model based on the degree-day approach. The model simulates the discharge and mass balance for glacierised Shaune Garang catchment. The degree-day factors for different land covers, used in the model, were estimated using daily stake measurements on Shaune Garang glacier and they were found to be varying between 2.6?±?0.4 and 9.3?±?0.3 mm °C^?1day^?1. The model is validated using observed discharge during ablation season of 2014 with coefficient of determination (R²) 0.90 and root mean square error (RMSE) 1.05 m³ sec^?1. The model is used to simulate discharge from 1985 to 2008 and mass balance from 2001 to 2008. The model results show significant contribution of seasonal snow and ice melt in total discharge of the catchment, especially during summer. We observe the maximum discharge in July having maximum contribution from snow and ice melt. The annual melt season discharge shows following a decreasing trend in the simulation period. The reconstructed mass balance shows mass loss of 0.89 m we per year between 2001 and 2008 with slight mass gain during 2000/01 and 2004/05 hydrological years.     

An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method

Daily inflow forecasts provide important decision support for the operations and management of reservoirs. Accurate and reliable forecasting plays an important role in the optimal management of water resources. Numerous studies have shown that decomposition integration models have good prediction capacity. Considering the nonlinearity and unsteady state of daily incoming flow data, a hybrid model of adaptive variational mode decomposition (VMD) and bidirectional long- and short-term memory (Bi-LSTM) based on energy entropy was developed for daily inflow forecast. The model was analyzed using the mean absolute error (MAE), the root means square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and correlation coefficient (r). A historical daily inflow series of the Baozhusi Hydropower Station, China, is investigated by the proposed VMD-BiLSTM with hybrid models. For comparison, BP, GRNN, ELMAN, SVR, LSTM, Bi-LSTM, EMD-LSTM, and VMD-LSTM, were adopted and analyzed for evaluation and analyzed. We found that the proposed model, with MAE?=?38.965, RMSE?=?64.783, and NSE?=?95.7%, was superior to the other models. Therefore, the hybrid model is robust and efficient for forecasting highly nonstationary and nonlinear streamflow. It can be used as the preferred data-driven tool to predict the daily inflow flow, which can ensure the safe operation of hydropower stations in reservoirs. As an interdisciplinary field spanning both machine learning and hydrology, daily inflow forecasting can become an important breakthrough in the application of deep learning to hydrology.

     

Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming

Estimation of suspended sediment yield is subject to uncertainty and bias. Many methods have been developed for estimating sediment yield but they still lack accuracy and robustness. This paper investigates the use of a machine-coded linear genetic programming (LGP) in daily suspended sediment estimation. The accuracy of LGP is compared with those of the Gene-expression programming (GEP), which is another branch of GP, and artificial neural network (ANN) technique. Daily streamflow and suspended sediment data from two stations on the Tongue River in Montana, USA, are used as case studies. Root mean square error (RMSE) and determination coefficient (R²) statistics are used for evaluating the accuracy of the models. Based on the comparison of the results, it is found that the LGP performs better than the GEP and ANN techniques. The GEP was also found to be better than the ANN. For the upstream and downstream stations, it is found that the LGP models with RMSE = 175 ton/day, R² = 0.941 and RMSE = 254 ton/day, R² = 0.959 in test period is superior in estimating daily suspended sediments than the best accurate GEP model with RMSE = 231 ton/day, R² = 0.941 and RMSE = 331 ton/day, R² = 0.934, respectively.     

Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling

The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.

     

Development and application of a simulation model for reservoir management

The success of irrigation system operation and planning depends on accurate quantification of supply and demand, and an equitable distribution of available water. The ultimate aim of this study was to determine how to meet the irrigation water demands if possible or to minimize the gap between the water supply and the demand. Most of the irrigation literature focuses only on the demand and the distribution aspects of this issue Irrigation projects that receive water from reservoirs, however, can be challenging to manage because the annual fluctuations in available water release from a reservoir can have a considerable impact on the irrigation management strategy. In real‐world situations, the reservoir operating rules guide reservoir operators in making actual water release decisions. This study develops a water balance simulation model for reservoir management, as well as testing it for Kangsabati Reservoir, West Bengal, India. Two rule curves for deciding irrigation water available from the reservoir were generated by taking the average and minimum stage values on a daily basis for a 16‐year period (1988–2003). Maintaining a minimum stage of 120.4 m throughout the year served as another rule to decide the release water available for irrigation. The minimum allowable stage of reservoir corresponding to a particular date of the year can be determined from those reservoir specific rule curves generated for irrigation purposes. The maximum permissible water release/outflow for irrigation from the test reservoir was taken as the volume of water available above the minimum allowable stage corresponding to the selected rule curve. The saturated hydraulic conductivity value (K_S) was calibrated to be 4.31 mm day^?1 for Kangsabati Reservoir.     

Prediction of Discharge Coefficient for Trapezoidal Labyrinth Side Weir Using a Neuro-Fuzzy Approach

Adaptive neuro-fuzzy inference system (ANFIS) is considered for flow over trapezoidal labyrinth side weirs located on a straight channel as a substantial part of distribution channels in irrigation systems and treatment units. To estimate the outflow over a trapezoidal labyrinth side weir, the discharge coefficient in the side weir equation needs to be determined in according with the effective dimensionless parameters which is Froude number, the sidewall angle, the ratios of weir length to channel width, weir length to total crest length and weir height to flow depth. 670 laboratory test results are used for determining discharge coefficient of trapezoidal labyrinth side weirs. The performance of the ANFIS model is compared with artificial neural networks (ANN), non-linear regression (NLR) and multi-linear regression (MLR) models. The comparing criteria used for the evaluation of the models’ performances are root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R²) statistics. Comparison results indicated that the ANFIS technique could be successfully employed in modeling discharge coefficient. It is found that the ANFIS model with RMSE of 0.090 in test period is superior in estimation of discharge coefficient than the nonlinear and linear regression models with RMSE of 0.124 and 0.279, respectively.