Nash-Sutcliffe Model Efficiency Research Articles

Application of PCSWMM for the 1-D and 1-D–2-D Modeling of Urban Flooding in Damansara Catchment, Malaysia

Coupled with climate change, the urbanization-driven increase in the frequency and intensity of floods can be seen in both developing and developed countries, and Malaysia is no exemption. As part of flood hazard mitigation, this study aimed to simulate the urban flood scenarios in Malaysia’s urbanized catchments. The flood simulation was performed using the Personal Computer Storm Water Management Model (PCSWMM) modeling of the Damansara catchment as a case study. An integrated hydrologic-hydraulic model was developed for the 1-D river flow modeling and 1-D–2-D drainage overflow modeling. The reliability of the 1-D river flow model was confirmed through the calibration and validation, in which the water level in TTDI Jaya was satisfactorily predicted, supported by the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), and relative error (RE). The performance of the 1-D–2-D model was further demonstrated based on the flood depth, extent, and risk caused by the drainage overflow. Two scenarios were tested, and the comparison results showed that the current drainage effectively reduced the drainage overflow due to the increased size of drains compared to the historic drainage in 2015. The procedure and findings of this study could serve as references for the application in flood mitigation planning worldwide, especially for developing countries.

Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables

AbstractStreamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prime (WM5P) techniques, as two robust artificial intelligence (AI) models, were applied for forecasting the monthly streamflow in Khoshkroud and Polroud Rivers located in two basins with the same names. Results of hybrid AI techniques were compared with those achieved by two stand-alone models of GEP and M5P. Seven combinations of hydrological (H) and meteorological (M) variables were considered to investigate the effect of climatic variables on the performance of the proposed techniques. Moreover, the performance of both stand-alone and hybrid models were evaluated by statistical criteria of correlation of coefficient, root-mean-square error, index of agreement, the Nash–Sutcliffe model efficiency coefficient, and relative improvement. The statistical results revealed that there is a dependency between ‘the M5P and GEP performance’ and ‘the geometric properties of basins (e.g., area, shape, slope, and river network density)’. It was found that a preprocessed technique could increase the performance of M5P and GEP models. Compared to the stand-alone techniques, the hybrid AI models resulted in higher performance. For both basins, the performance of the WM5P model was higher than the WGEP model, especially for extreme events. Overall, the results demonstrated that the proposed hybrid AI approaches are reliable tools for forecasting the monthly streamflow, while the meteorological and hydrometric variables are taken into account.

Impact of Stream-Groundwater Interactions on Peak Streamflow in the Floods

Floods are the one of the most significant natural disasters, with a damaging effect on human life and properties. Recent global warming and climate change exacerbate the flooding by increasing the frequency and intensity of severe floods. This study explores the role of groundwater during the floods at the Miho catchment in South Korea. The Hydrological-Ecological Integrated watershed-scale Flow model (HEIFLOW) model is used for the flood simulations to investigate the impact of groundwater and streamflow interactions during floods. The HEIFLOW model is assessed by the Nash–Sutcliffe model efficiency coefficient (NSE) and Root Mean Square Error (RMSE) for the surface water and groundwater domains, respectively. The model evaluation shows the acceptable model performance (0.64 NSE and 0.25 m–2.06 m RMSE) with the hourly time steps. The HEIFLOW shows potential as one of the methods for the flood risk management in South Korea. The major findings of this study indicate that the stream runoff at the Miho catchment is highly affected by the groundwater flows during the dry and flood seasons. Thus, the interactions between surface water and groundwater domains should be fully considered to mitigate the water hazards at the catchment scale.

Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea

Prediction of rice yields at pixel scale rather than county scale can benefit crop management and scientific understanding because it is useful for monitoring how crop yields respond to various agricultural systems and environmental factors. In this study, we propose a methodology for the early prediction of rice yield at pixel scale combining a crop model and a deep learning model for different agricultural systems throughout South and North Korea. Initially, satellite-integrated crop models were applied to obtain a pixel-scale reference rice yield. Then, the pixel-scale reference rice yields were used as target labels in the deep learning model to leverage the advantages of crop models. Models of five different deep learning network architectures were employed to help determine the hybrid structure of long-short term memory (LSTM) and one-dimensional convolutional neural network (1D-CNN) layers by predicting the optimal model about two months ahead of harvest time. The suggested model showed good performance [R2 = 0.859, Nash-Sutcliffe model efficiency = 0.858, root mean squared error = 0.605 Mg ha−1], with specific spatial patterns of rice yields for South and North Korea. Analysis of the relative importance of the input variables showed the water-related index and maximum temperature in North Korea and the vegetation indices and geographic variables in South Korea to be crucial for predicting rice yields. The proposed approach successfully predicted and diagnosed rice yield at the pixel scale for inaccessible locations where reliable ground measurements are not available, especially North Korea.

Performance of HEC-HMS and SWAT to simulate streamflow in the sub-humid tropical Hemavathi catchment

Abstract The present study was conducted to examine the accuracy and applicability of the hydrological models Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center (HEC)- Hydrologic Modeling System (HMS) to simulate streamflows. Models combined with the ArcGIS interface have been used for hydrological study in the humid tropical Hemavathi catchment (5,427 square kilometer). The critical focus of the streamflow analysis was to determine the efficiency of the models when the models were calibrated and optimized using observed flows in the simulation of streamflows. Daily weather gauge stations data were used as inputs for the models from the 2014–2020 period. Other data inputs required to run the models included land use/land cover (LU/LC) classes resulting from remote sensing satellite imagery, soil map and digital elevation model (DEM). For evaluating the model performance and calibration, daily stream discharge from the catchment outlet data were used. For the SWAT model calibration, available water holding capacity by soil (SOL_AWC), curve number (CN) and soil evaporation compensation factor (ESCO) are identified as the sensitive parameters. Initial abstraction (Ia) and lag time (Tlag) are the significant parameters identified for the HEC-HMS model calibration. The models were subsequently adjusted by autocalibration for 2014–2017 to minimize the variations in simulated and observed streamflow values at the catchment outlet (Akkihebbal). The hydrological models were validated for the 2018–2020 period by using the calibrated models. For evaluating the simulating daily streamflows during calibration and validation phases, performances of the models were conducted by using the Nash-Sutcliffe model efficiency (NSE) and coefficient of determination (R2). The SWAT model yielded high R2 and NSE values of 0.85 and 0.82 for daily streamflow comparisons for the catchment outlet at the validation time, suggesting that the SWAT model showed relatively good results compared to the HEC-HMS model. Also, under modified LU/LC and ungauged streamflow conditions, the calibrated models can be later used to simulate streamflows for future predictions. Overall, the SWAT model seems to have done well in streamflow analysis for hydrological studies.

Improving the performance of rainfall-runoff models using the gene expression programming approach

Abstract In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate.

Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm

Determination of wetting patterns’ dimensions is essential in designing and managing surface/subsurface drip irrigation systems. The laboratory experiments were conducted using physical model with dimensions of 3 × 1 × 0.5 m3 to evaluate the moisture redistribution process under continuous and pulse surface/subsurface irrigation systems. In the present study, the efficiency of a new machine learning method, named fuzzy c-means clustering- based adaptive neural-fuzzy inference system combined with a new meta-heuristic algorithm, hybrid particle swarm optimization – gravity search algorithm (ANFIS-FCM-PSOGSA), is investigated in order to model wetting front redistribution of drip irrigation systems (IS) using soil and system parameters as inputs under continuous and pulse surface/subsurface IS. The outcomes of the assessed method are compared with those of the ANFIS-FCM-PSO, generalized regression neural networks and multivariate adaptive regression splines. In assessing the implemented methods, four commonly used indices, root mean square errors (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe model efficiency (NSE) and graphical methods (e.g., scatter, box plot and Taylor diagrams) are utilized. The benchmark outcomes demonstrate the superiority of new method in estimating wetting front dimensions by improving the accuracy of the ANFIS-FCM-PSO by 29.6%, 18.5%, 6.1%, and 9.0% in estimating the diameter of horizontal redistribution with respect to RMSE, MAE, R2 and NSE, respectively. Furthermore, the ANFIS-FCM-PSOGSA respectively improves the RMSE, MAE, R2 and NSE accuracy of the ANFIS-FCM-PSO by 20.1%, 19.2%, 35.7% and 35.6% in estimating the diameter of downward vertical redistribution. The general outcomes recommend the use of new method in estimating wetting front dimensions of drip irrigation systems.

Calibration and Validation of HEC-HMS Model for Chalakudy River Basin

Prediction of flood prone areas in a basin and evolution of the impact of climate change on water resources needs a correct estimation of the availability of water which will solely be achieved by hydrological modelling of the basin. However, modelling the hydrology of a basin is a complex task and models should be well calibrated to increase user confidence in its predictive ability which in turn makes the application of the model effective. In this study rainfall-runoff simulation model viz., Hydrologic Modelling System, developed by the Hydrologic Engineering Centre USA (HEC-HMS) has been calibrated and validated for Chalakduy river basin in Kerala, in Sothern India for prediction of its hydrologic response. The result shows Curve Number (CN), Lag time and initial abstraction (Ia) to be the sensitive parameters for the simulated stream flow. The statistical analysis of Nash-Sutcliffe model efficiency criteria, the percentage error in peak, percentage error in volume, and net difference of observed and simulated time to peak, which were used for performance evaluation, have been found to range from (0.70 to 0.87), (4.39 to 19.47%), (1.9 to 19%) and (0 to 1day) respectively, indicating a very good performance of the model for simulation of stream flow.

Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniques

AbstractThis study was designed to evaluate the ability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, LR, and MLR to predict the relative energy dissipation (ΔE/Eu) for vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation. In the experiments, the discharge rate, drop height, and porosity of the screens were varied. Parameters yc/h, yd/yc, and p were input variables, and ΔE/Eu was the output variable. The efficiencies of the models were compared using the following metrics: correlation coefficient (CC), mean absolute error (MAE), root-mean-square error (RMSE), Normalized root mean square error (NRMSE) and Nash–Sutcliffe model efficiency (NSE). Results indicate that the performance of the ANFIS_gbellmf based model with a CC value of 0.9953, RMSE value of 0.0069, MAE value of 0.0042, NRMSE value as 0.0092 and NSE value as 0.9895 was superior to other applied models. Also, a linear regression yielded CC = 0.9933, RMSE = 0.0083, and MAE = 0.0067. This linear model outperformed multiple linear regression models. Results from a sensitivity study suggest that yc/h is the most effective parameter for predicting ΔE/Eu.

An Improved Multivariate Weather Prediction Model Using Deep Neural Networks and Particle Swarm Optimisation

Accurate weather prediction is always a challenge for meteorologists. This paper suggests a Deep Neural Network (DNN) model to predict minimum and maximum values of temperature based on various weather parameters such as humidity, dew point, and wind speed. Particle Swarm Optimisation (PSO) algorithm is applied to select relevant and important features of the datasets to improve the prediction accuracy of the model. The grid search algorithm is employed to determine the hyperparameters of the proposed DNN model. The statistical indicators Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, Nash–Sutcliffe model efficiency coefficient, and Correlation Coefficient are used to evaluate the accuracy of the prediction model. Performance comparison of the proposed model is performed with the Support Vector Machine (SVM) and Vector Autoregression (VAR) models. The experimental outcomes show that the proposed model optimised using PSO achieves better prediction accuracy than traditional approaches.

The HADES Yield Prediction System - A Case Study on the Turkish Hazelnut Sector.

Crop yield forecasting activities are essential to support decision making of farmers, private companies and public entities. While standard systems use georeferenced agro-climatic data as input to process-based simulation models, new trends entail the application of machine learning for yield prediction. In this paper we present HADES (HAzelnut yielD forEcaSt), a hazelnut yield prediction system, in which process-based modeling and machine learning techniques are hybridized and applied in Turkey. Official yields in the top hazelnut producing municipalities in 2004–2019 are used as reference data, whereas ground observations of phenology and weather data represent the main HADES inputs. A statistical analysis allows inferring the occurrence and magnitude of biennial bearing in official yields and is used to aid the calibration of a process-based hazelnut simulation model. Then, a Random Forest algorithm is deployed in regression mode using the outputs of the process-based model as predictors, together with information on hazelnut varieties, the presence of alternate bearing in the yield series, and agro-meteorological indicators. HADES predictive ability in calibration and validation was balanced, with relative root mean square error below 20%, and R2 and Nash-Sutcliffe modeling efficiency above 0.7 considering all municipalities together. HADES paves the way for a next-generation yield prediction system, to deliver timely and robust information and enhance the sustainability of the hazelnut sector across the globe.

Prediction of long-term inter-seasonal variations of streamflow and sediment load by state-space model in the Loess Plateau of China

Streamflow and sediment load exporting from catchments usually show high inter-variability caused by climate variability and land surface changes. It is challenge to predict monthly or daily streamflow and sediment load using simply statistical methods. The state-space model is an effective tool for quantifying localized variations, and it only needs some easily obtained climate and land surface variables. However, there is no state-space model developed to predict the long-term variations of monthly streamflow, sediment load and suspended sediment concentration. In this study, fourteen main catchments in the Loess Plateau of China were chosen, and these catchments experienced abrupt reductions of annual streamflow (Q), specific sediment yield (SSY) and suspended sediment concentration (SSC) during the past five decades. The state-space model was developed to predict monthly Q, SSY and SSC during 1982–2011 by precipitation, potential evapotranspiration and normalized difference vegetation index. The results indicated that the monthly Q, SSY and SSC in summer season decreased significantly in nearly all the catchments (p < 0.01). The periodicity of monthly Q, SSY and SSC reduced, and especially the periodicity was not detected after 2003. The precipitation and previous Q were the two main state variables to estimate monthly Q with transition coefficients of 0.56 and 0.25, respectively, whereas Q and previous SSC were the most important state variables to estimate monthly SSY and SSC with transition coefficients of 0.50 and 0.34, respectively. The state-space model was satisfactory to simulate monthly Q, SSY and SSC with Nash-Sutcliffe model efficiency (NSE) over 0.85 in model calibration and validation. Furthermore, the model performance was best for predicting monthly Q, followed by predicting monthly SSY and SSC with the average NSE of 0.90, 0.89 and 0.87, respectively. The reductions of monthly streamflow and sediment load were mainly derived by soil and water conservation measures (SWCMs) and especially check-dams. The catchment runoff and sediment coefficient decreased significantly and linearly with the percentages of area affected by SWCMs (p < 0.01). This study indicates that the state-space model is a useful tool to predict streamflow and sediment load time series with the hydrological and land surface informations.

Evaluating Instream Restoration Effectiveness in Reducing Nitrogen Export from an Urban Catchment with a Data‐Model Approach

AbstractUrbanization increases stormwater runoff into streams, resulting in channel erosion, and increases in sediment and nutrient delivery to receiving water bodies. Stream restoration is widely used as a Best Management Practice to stabilize banks and reduce sediment and nutrient loads. While most instream nutrient retention measurements are often limited to low flow conditions, most of the nutrient load is mobilized at high stream flows in urban settings. We, therefore, use a process‐based stream ecosystem model in conjunction with measurements at low flows and focus on estimation of stream nitrogen retention over the full streamflow distribution. The model provides a theoretical framework to evaluate the geomorphic, hydrologic, and ecological factors that are manipulated by stream restoration, and drive nitrogen retention. We set a model for a pool‐riffle sequence restored stream (190 m) in Baltimore County, Maryland and calibrated the model to the in situ measured primary production (Nash–Sutcliffe model efficiency coefficient [NSE] NSE = 0.89), respiration (NSE = 0.74), and nitrate uptake lengths (R2 = 0.88). At the daily scale, simulations showed low nitrogen retention during high flows due to high transport rates, mobilization of stored hyporheic nitrogen, and scouring of periphyton biomass. This result underscores the need to reduce contributing watershed runoff flashiness to promote aquatic nutrient cycling and retention. At monthly and yearly time scale, model predicted a higher percent reduction in summer than in winter and estimated 5.7%–9.5% of annual nitrate reductions. While the model was tested in a pool‐riffle sequence restoration design, the approach can be adapted to evaluate a range of channel restoration design characteristics, and the effects of upland watershed restoration to mitigate stormwater loading through both restored and unrestored streams.

Random forest regression results in accurate assessment of potato nitrogen status based on multispectral data from different platforms and the critical concentration approach

Remote sensing can be used for precision nutrient management to assess plant nitrogen (N) status in a spatially detailed and real-time manner. Despite recent advances in satellite- and drone technology and machine learning, neither differences between platforms nor methodological aspects for estimating plant N status have been sufficiently investigated. In this study, multispectral data obtained by ground (handheld Rapidscan), air- (unmanned aerial vehicle, UAV) and spaceborne (Sentinel-2) platforms were exploited to estimate plant N uptake (PNU), concentration (PNC) and N nutrition index (NNI). The test plant was potato grown for three years on a sandy soil in Denmark and the analysis was based on the critical N dilution curve. Parametric (PR) and non-parametric (random forest, RFR) regressions were conducted and compared in predicting mid-season PNU, PNC and NNI from band reflectances or vegetation indices (VIs) derived from each platform data. The results obtained by the UAV data had the highest accuracy, largely due to the fine spatial resolution. For both regression types, PNU and NNI correlated better than PNC to reflectance data. For the UAV data, validation Nash-Sutcliffe model efficiency (NSE) of PNU and NNI ranged between 0.64–0.95 and 0.41–0.92 respectively, with corresponding values for relative root mean square error (RRMSE) of 7.1–22% and 5.86–22%. The lower end of NSE and higher end RRMSE intervals systematically being from the PR, which demonstrates the robustness and the high accuracy of RFR in predicting plant N status. The other platforms resulted in acceptable results, with validation NSE and RRMSE for PNU and NNI of, respectively, 0.60–0.79 and 14–20%, 0.25–0.79 and 10–17% for Rapidscan, and 0.48–0.83 and 17–28%, 0.42–0.82 and 12–19% for Sentinel-2. The band reflectance and the VIs were equally suited as input predictors for the RFR algorithm. The N requirement calculated from all three datasets reflected the field observations well. The study reveals the potential of different regression methods for detailed spatial estimation of plant N status to guide in-season fertilization by matching the plant growth demands, emphasizing the strengths of the RFR. The procedure is helpful for the digital agriculture and the smart farming industry aiming to avoid excess application of N.

The overall framework design of automatic logistics system using a hybrid ANN-PSO model

Automated logistic systems are commonly applied in the enterprise logistic processes while allowing to improve the reliability and efficiency of logistics procedures with computer simulation. The logistics system focuses on realizing the space and time efficiency of material and understanding the links of logistics to achieve the optimum economic effect. Therefore, in this study, 150 data were collected from 20 hospitals in Malaysia to adopt the automated logistic system under simulation of the computer to decrease the costs of hospitals and increase the serviceability (output). Since obtaining this information for the automated logistic systems is not easy and has a high cost in a traditional way, this study was performed by the integration of artificial neural network (ANN) and particle swarm optimization (PSO) as the ANN-PSO. The input data that were obtained from the processing of automated logistic system in hospitals verified by using a hybrid of ANN-PSO. Later, the results were measured with five regression indicators of determination coefficient (R2), root mean square (RMSE), Pearson correlation coefficient (r), Nash–Sutcliffe model efficiency coefficient (NSE) and Willmott’s index (WI). Considering the results of RMSE, RSQR and r in both testing and training phases, the good performance of ANN-PSO in determining the effectiveness of applying automated logistic system using computer simulation method in hospital management, in terms of raising its serviceability and reducing the costs was proved. As a result, it was shown that ANN-PSO can successfully determine the effectiveness of using an automated logistic system under computer simulation method in hospital management.

Modelling hybrid and backpropagation adaptive neuro-fuzzy inference systems for flood forecasting

The ability of the adaptive neuro-fuzzy inference algorithm architecture to simulate floods is explored in this research. The development of models for flood forecasting has been centered on two adaptive neuro-fuzzy inference (ANFIS) algorithms. The Takagi–Sugeno fuzzy inference systems (FIS) generated through subtracted clustering were trained using hybrid and backpropagation training algorithms. Multiple statistical performance evaluators were used to assess the performability of the established models. The validity and predictive power of the models are evaluated by estimating a flood occurrence in the study area. In designing the models, a total of 12 inputs were employed. The best performability was found for the ANFIS model created utilizing a hybrid training algorithm with mean square error (MSE) of 0.00034, co-efficient of correlation (R2) of 97.066%, root mean square error (RMSE) of 0.018, Nash–Sutcliffe model efficiency (NSE) of 0.968, mean absolute error (MAE) of 0.0073 and combined accuracy (CA) of 0.018, indicating the possible usage of exploiting the established model for prediction of floods.

Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.

Approximation of the discharge coefficient of differential pressure flowmeters using different soft computing strategies

Due to its importance in flow measurement and instrumentation, as well as its frequent application in differential pressure flowmeters, orifice discharge coefficient (Cd) needs to be estimated precisely. In this study, different soft computing models (including multiple linear regression (MLR), group method of data handling (GMDH), multivariate adaptive regression splines (MARS), M5P tree model, and random forest (RF)) were employed for the first time in estimation of the Cd value, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), scattering index (SI), and Nash–Sutcliffe model efficiency coefficient (NSE) were used as the statistical indicators for validating the performance of each soft computing model. The statistical indicators approved the superiority of the RF model over the other models, while the MARS model also showed a competitive prediction potential over M5P, GMDH, and MLR models. The findings of this computational study clearly demonstrated that the implemented soft computing strategy had the capability to be used in precise estimation of the Cd of the orifice meter, specifically, in situations where the measurement of the parameters in deterministic equation is not practically feasible.

Calibration and validation of hydrological model using HEC-HMS for Kuantan River Basin

Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. This study aims to calibrate and validate the rainfall-runoff transformation model for Bukit Kenau Station in Kuantan River Basin (KRB) using HEC-HMS (Hydrologic Engineering Centre Hydrology Modeling System). For the loss rate, SCS Curve Number method was selected while Clark Unit Hydrograph was used for the transform method. The model calibration and validation efficiency were verified using the Nash-Sutcliffe model efficiency (ME). As a result, the model calibration and validation were found to be satisfactory with ME between 0.5 to 0.8. The model can be used to forecast the river flow and helps in the flood mitigation works to reduce the impacts along with the cost use. Besides, the result obtained from this study can be used as a guideline for future flood risk assessment works in the study area.

Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

Bursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.