Nash-Sutcliffe Efficiency Research Articles

Integration of UAV Digital Surface Model and HEC-HMS Hydrological Model System in iRIC Hydrological Simulation—A Case Study of Wu River

This research investigates flood susceptibility in the mid- and downstream areas of Taiwan’s Wu River, historically prone to flooding in central Taiwan. The study integrates the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) for flow simulations with unmanned aerial vehicle (UAV)-derived digital surface models (DSMs) at varying resolutions. Flood simulations, executed through the International River Interface Cooperative (iRIC), assess flood depths using diverse DSM resolutions. Notably, HEC-HMS simulations exhibit commendable Nash–Sutcliffe efficiency (NSE) exceeding 0.88 and a peak flow percentage error (PEPF) below 5%, indicating excellent suitability. In iRIC flood simulations, optimal results emerge with a 2 m resolution UAV-DSM. Furthermore, the study incorporates rainfall data at different recurrence intervals in iRIC flood simulations, presenting an alternative flood modeling approach. This research underscores the efficacy of integrating UAV-DSM into iRIC flood simulations, enabling precise flood depth assessment and risk analysis for flood control management.

Assessment of synthetic series to design the capacity of flow regulation reservoirs

Freshwater scarcity is perceived as a global systemic risk. Intending to reach water secutiry, flow regulation reservoirs are alternatives to improve streamflow and water storage, especially in places where demand exceeds water availability during periods of drought. However, the unavailability of historical series of flows in watersheds makes the adequate design of reservoirs difficult. To overcome such difficulties, synthetic series of flows are currently being generated for the specific sections of the watercourses where these structures are to be designed. The present study aimed to evaluate two methods of generating synthetic series of flows applied to design the capacity of flow regulation reservoirs in conditions of limited data. A monitored hydrological region (Itapemirim River Basin, Espírito Santo, Brazil) and synthetic flow series generated according to the methodologies of Rodrigues (2017) and Cesconetto (2021) were used. The methodologies were tested by cross-validation, and the results were compared using graphs and statistical indicators to the results obtained with observed data, measured in fluviometric reference stations. After analyzing the statistical indicators, such as the coefficient of determination (R2), the Nash-Sutcliffe efficiency index (NSE), and the reservoir design quality index (IQD), it was concluded that the method proposed by Cesconetto (2021) showed a tendency to under-design the capacities of the reservoirs, mainly for lower flows to be regularized (β < 0.75). The method proposed by Rodrigues (2017) performed better than the method proposed by Cesconetto (2021) for the design of the capacity of flow regulation reservoirs under conditions of limited data in the Itapemirim River Basin.

Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania

Study regionThis study refers to the Wami river sub-catchments in Eastern Tanzania. Study FocusThe five-machine learning (ML) algorithms, including long short-term memory (LSTM), multivariate adaptive regression spline (MARS), support vector machine (SVM), extreme learning machine (ELM), and M5 Tree, were used to predict the most widely used drought index, the standard precipitation index (SPI), at six and nine months. Algorithms were established using monthly rainfall data for the period from 1990 to 2022 at five meteorological stations distributed across the Wami River sub-catchment: Barega, Dakawa, Dodoma, Kongwa, and Mandera stations. New hydrological insights for the region. The predicted results of all five ML algorithms were evaluated using several statistical metrics, including Pearson’s correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and Nash Sutcliffe efficiency (NSE). The prediction results revealed that LSTM perform better in predicting drought conditions using SPI6 (6-month SPI) and SPI9 (9-month SPI) with the highest NSE of 0.99 in all five stations, and R of 0.99 in four stations except at Kongwa station, where R range from 0.75 to 0.99. These prediction results will aid decision-makers and planners to develop a drought monitoring and drought early warning system in order to strengthen the governance and resilience to the catchment and people on the impacts of water scarcity and climate change.

An approach for flood flow prediction utilizing new hybrids of ANFIS with several optimization techniques: a case study

ABSTRACT Using machine learning methods is efficient in predicting floods in areas where complete data is not available. Therefore, this study considers the Adaptive Neuro-Fuzzy Inference System (ANFIS) model combined with evolutionary algorithms, namely Harris Hawks Optimization (HHO) and Arithmetic Optimization Algorithm (AOA), to predict the flood of Shahrchay River in the northwest of Iran. The data used included the daily data of precipitation, evaporation, and runoff in the years 2016 and 2017, where 70% of the data were used for model training and the rest for testing the models. The results showed that although the ANFIS model provided values with high errors in several steps, especially in steps with maximum or minimum values, the use of HHO and AOA optimization algorithms resulted in a significant reduction in the error values. The ANFIS-AOA model utilizing an input scenario including the flow in the previous one to three days exerted the most promising results in the test data, with Nash Sutcliffe Efficiency (NSE) Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) of 0.93, 1.34, and 0.69, respectively. According to Taylor's diagram, the ANFIS-AOA hybrid algorithm predicts flood values with greater performance than the other models.

Crop Yield Estimation using Improved Salp Swarm Algorithm based Feature Selection

Crop yield estimation is the art of yield prediction before harvest and it is essential for planning and making conclusive agricultural policies. The forecasting of crop yield is essential in optimal nutrient management, crop insurance, crop market planning and harvest management. However, the crop yield estimation is considered as a challenging task because of huge amount of abundant information exists in the crop data. Therefore, an effective feature selection is required to be developed for removing the redundant attributes. In this research, an Improved Salp Swarm Algorithm (ISSA) based feature selection for an effective crop yield estimation. The Opposition Based Learning (OBL) and Local Search Algorithm (LSA) are incorporated in the ISSA’s initialization and exploitation phase for selecting optimum feature subset. The selected features from the ISSA are used to enhance the classification using Modified Long Short Term Memory (MLSTM) classifier. The performance of the ISSA-MLSTM is analyzed using accuracy, precision, recall, F-score, Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The existing researches such as Ensemble approach and MLSTM are used to evaluate the ISSA-MLSTM. The accuracy of the ISSA-MLSTM is 99% that is high when compared to the MLSTM.

The impact of environmental variables on surface Conductance: Advancing simulation with a nonlinear Machine learning model

Surface conductance (Gs) is a key factor in the Penman-Monteith (PM) equation; the interaction between environmental variables such as CO2 concentration, air temperature (TA), vapor pressure deficit (VPD), soil water content (SWC), and net radiation (R) affects Gs, evapotranspiration and thus impacts the hydrological cycle. These interactions are highly nonlinear and vary among different vegetation types. However, conventional Gs simulation models use fixed interactions between environmental variables in their equations for all vegetation types. Moreover, the characterisation and parameterisation of conventional Gs models is highly uncertain due to the high spatiotemporal variability in key environmental variables and plant parameters, which inhibits their generalisation. This study investigates whether Gs could be estimated more accurately by nonlinear statistical techniques that capture the multiple interactions between the environmental variables that affect Gs for each vegetation type. We compare mixed generalized additive model (MGAM) for Gs simulation with semi-empirical and empirical models at 20 eddy covariance flux tower sites with four different vegetation types at daily and monthly timescales. The results show that the Nash-Sutcliffe Efficiency (NSE) in Gs simulation increased by up to 50% in MGAM model in comparison to the semi-empirical and empirical models. The MGAM model highlighted the interactive effects of CO2, VPD, and SWC for crops and grasses. The interactive effects of CO2, VPD, and TA were important for trees and grasses. The results from this study expand our understanding of the ability of Gs simulation models to identify and include the interactive effects of crucial environmental variables on plant transpiration and hydrological processes.

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling

A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall–runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network. In the proposed framework, multiple layers of the CNN component process long-term hourly meteorological time series data, while the LSTM component handles short-term meteorological time series data and utilizes the extracted features from the 1D-CNN. In order to demonstrate the effectiveness of the proposed approach, it was implemented for hourly rainfall–runoff modeling in the Ishikari River watershed, Japan. A meteorological dataset, including precipitation, air temperature, evapotranspiration, longwave radiation, and shortwave radiation, was utilized as input. The results of the proposed approach (CNNsLSTM) were compared with those of previously proposed deep learning approaches used in hydrologic modeling, such as 1D-CNN, LSTM with only hourly inputs (LSTMwHour), a parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture, which uses both daily and hourly input data (LSTMwDpH). Meteorological and runoff datasets were separated into training, validation, and test periods to train the deep learning model without overfitting, and evaluate the model with an independent dataset. The proposed approach clearly improved estimation accuracy compared to previously utilized deep learning approaches in rainfall = runoff modeling. In comparison with the observed flows, the median values of the Nash–Sutcliffe efficiency for the test period were 0.455–0.469 for 1D-CNN, 0.639–0.656 for CNNpLSTM, 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865–0.873 for the proposed CNNsLSTM. Furthermore, the proposed CNNsLSTM reduced the median root mean square error (RMSE) of 1D-CNN by 50.2%–51.4%, CNNpLSTM by 37.4%–40.8%, LSTMwHour by 27.3%–29.5%, and LSTMwDpH by 10.6%–13.4%. Particularly, the proposed CNNsLSTM improved the estimations for high flows (≧75th percentile) and peak flows (≧95th percentile). The computational speed of LSTMwDpH is the fastest among the five architectures. Although the computation speed of CNNsLSTM is slower than LSTMwDpH's, it is still 6.9–7.9 times faster than that of LSTMwHour. Therefore, the proposed CNNsLSTM would be an effective approach for flood management and hydraulic structure design, mainly under climate change conditions that require estimating hourly river flows using meteorological datasets.

Quantifying the influence of climate change on streamflow of Rietspruit sub-basin, South Africa

ABSTRACT This study integrated climate projections from five global climate models (GCMs) into the soil and water assessment tool to evaluate the potential impact of climate alterations on the Rietspruit River sub-basin under two representative concentration pathways (RCP4.5 and 8.5). The model's performance was evaluated based on the coefficient of determination (R2), percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), probability (P)-factor and correlation coefficient (R)-factor. Calibration results showed an R2 of 0.62, NSE of 0.60, PBIAS of 20, P-factor of 0.86 and R-factor of 0.91, while validation produced an R2 of 0.64, NSE of 0.61, PBIAS of 40, P-factor of 0.85 and R-factor of 1.22. Precipitation is predicted to increase under both RCPs. Maximum temperature is projected to increase under both RCPs, with a major increase in the winter months. Minimum temperatures are projected to decrease under RCP4.5 in the near (−0.99 °C) and mid (−0.23 °C) futures, while the far future is projected to experience an increase of 0.14 °C. Precipitation and temperature changes correspond to increases in streamflow by an average of 53% (RCP4.5) and 47% (RCP8.5). These results indicate a need for an integrated approach in catchment water resource management amid potential climate and land use variations.

Unveiling hydrological dynamics in data-scarce regions: experiences from the Ethiopian Rift Valley Lakes Basin

Abstract. The hydrological system of the Rift Valley Lakes in Ethiopia has recently experienced changes over the past 2 decades. Potential causes for these changes include anthropogenic, hydro-climatic, and geological factors. The main objective of this study was to utilize an integrated methodology to gain a comprehensive understanding of the hydrological systems and potential driving factors within a complex and data-scarce region. To this end, we integrated a hydrologic model, change point analysis, indicators of hydrological alteration (IHA), and a bathymetry survey to investigate hydrological dynamics and potential causes. A hydrologic model (the Soil and Water Assessment Tool Plus, SWAT+) was parameterized for the gauged watersheds and extended to the ungauged watersheds using multi-site regionalization techniques. The SWAT+ model performed very well to satisfactorily for daily streamflow in all watersheds with respect to the objective functions of the Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). The findings reveal notable changes in lake inflows and lake levels over the past 2 decades. Lake Chamo experienced an increase in area of 30.1 km2 (9.5 %), an increase in depth of 4.4 m (30.9 %), and an increase in volume of 7.8×108 m3 (27.2 %). In contrast, Lake Abijata witnessed an extraordinary 68 % decrease in area and a depth decrease of 1.6 m (37.2 %). During the impact period, the mean annual rainfall experienced a decrease of 6.5 % and 2.7 % over Lake Abijata and Lake Chamo, respectively. Actual evapotranspiration decreased by 2.9 % in Lake Abijata but increased by up to 4.5 % in Lake Chamo. Surface inflow to Lake Abijata decreased by 12.5 %, while Lake Chamo experienced an 80.5 % increase in surface inflow. Sediment depth in Lake Chamo also increased by 0.6 m (4.2 %). The results highlight that the changing hydrological regime in Lake Chamo is driven by increased surface runoff and sediment intrusion associated with anthropogenic influences. The hydrological regime of Lake Abijata is affected by water abstraction from feeding rivers and lakes for industrial and irrigation purposes. This integrated methodology provides a holistic understanding of complex data-scarce hydrological systems and potential driving factors in the Rift Valley Lakes in Ethiopia, which could have global applicability.

Effect of Evapotranspiration on Soil Moisture Dynamics in Top Surface Layer of a Loamy Land in Climate Change Condition

Abstract Evapotranspiration affects uncertain changes in volumetric soil moisture content (θ) of earth surface, which is considerably controlled by temporal variability of weather parameters like rainfall and ambient temperature. Accurate measurement of temporal variation and spatial distribution of θ in a particular land is very challenging. Numerical modelling with any suitable computer code might be useful in such cases. Thus, Hydrus 2D modelling of θ variation in the soil at Odisha University of Agriculture and Technology (OUAT) in Bhubaneswar is undertaken as main objective of present study to investigate soil moisture dynamics in top surface layer. For the study, the θ in OUAT land was measured daily by 5 TM water content sensor for the duration of two years spanning from January 2021 to December 2022. Meteorological data for these 2 years are collected from a nearby weather station at OUAT and used for calculating evapotranspiration (ET) based on five different well known ET models. Soil hydraulic parameters of OUAT land were also evaluated by laboratory investigation. The evapotranspiration so calculated along with precipitation and materials properties were then assigned as the inputs in Hydrus 2D simulations. The simulated results are found to be in good agreement with field observations. It is proven by Pearson’s coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) which are found to be 0.83 and 0.84 respectively. The soil moisture simulation was the most accurate only when measured soil parameters along with atmospheric boundary involving Penman-Monteith (PM) ET model were considered as model inputs.

Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model.

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

Investigating the projected changes in water balance components under climate change considering the effect of storage structures

ABSTRACT The objective of this study was the critical challenge of accurately predicting water balance components in the Upper Bhima River basin, which is also facing significant challenges due to climate change. A major challenge faced in water balance studies is inadequacy of existing hydrological models to account for the effects of storage structures. The study utilized the variable infiltration capacity–routing application for parallel computation of discharge hydrological model with a newly developed storage structure scheme to simulate water balance components for historical (1999–2010) and future (2019–2040) periods, with future climate forcing from 19 Coupled Model Intercomparison Project Phase 5 GCMs under Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios. The performance of the model was evaluated against observed streamflow data and around 30% improvement is noticed for the Nash–Sutcliffe efficiency score. The results signify the adverse impacts of climate change in the region, particularly a significant decrease in monsoon precipitation which may intensify drought scenarios and affect monsoon-driven agriculture. Furthermore, the study emphasizes the high sensitivity of baseflow in the Upper Bhima River to climate alterations, indicating potential threats to biodiversity and river ecosystem health. This research offers indispensable findings crucial for future strategies concerning hydropower, flood management, and water resource management in the region.

Energy-economic-environmental analysis of a net-zero energy greenhouse with fan-coil units and hot-water pipes: Experiment and modelling

This study analysed the performance of a hot-water pipe (HWP) and fan-coil units (FCUs) in controlling microclimate conditions in a net-zero energy greenhouse (NZEG). The performance of a previously developed TraNsient SYstem Simulation (TRNSYS) model for the NZEG was enhanced to include a crop model and several HWP emission standards, and the improved TRNSYS model was used to analyse heating, ventilation and air-conditioning (HVAC) systems for different greenhouse sizes in the Republic of Korea. Further, an economic analysis was conducted. The model validation showed excellent agreement between the measured and simulated values, with Nash-Sutcliffe efficiency of 0.98. The presence of crop in the simulation model reduced the total heating load by 14.5 MWh and increased the cooling load by 4 MWh. The capacity of the FCU was increased as the outside temperature rose from 33.4[Formula: see text] to 36.4[Formula: see text] in summer, and the diameter of the HWP was decreased from 25A to 10A as the outside temperature rose from −21.7[Formula: see text] to −12.2[Formula: see text] in winter to maintain the greenhouse at the desired temperature of 15[Formula: see text]. Notably, the payback period was reduced by 9 years when the annual savings in heating energy costs and revenue from carbon trading were considered as a part of the net economic cash flow.

Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India

Groundwater serves as a vital resource for all living organisms. In regions extensively reliant on groundwater irrigation, hydro-climatic factors, groundwater extraction, and the flow of surface water exhibit an indirect interdependence. This study primarily aims to anticipate GWL in such highly irrigated zones using the Machine Learning (ML) approach. To achieve this, the widely employed Random Forest (RF), Bagging-Reduce Error Pruning Tree (Bagging-REPTree), and Bagging-Decision Stump Tree (Bagging-DSTree) models have been employed for the accurate forecasting of groundwater levels. The long-term pre-monsoon and post-monsoon (fourteen locations) data set of South-Central Punjab state has been applied for the model calibration/training and validation/testing. Seven statistical indices were used such as percent bias (PBIAS), root mean square error (RMSE), normalized root mean square error (nRMSE), RMSE-observation standard deviation ratio (RSR), mean absolute error (MAE), Nash Sutcliffe efficiency (NSE) and correlation coefficient (CC) for the model performance analysis. The results revealed that the RF model outperformed in pre-monsoon (testing phase) (RMSE = 0.682, NSE = 0.958) as well as the post-monsoon (testing phase) (RMSE = 0.150, NSE = 0.997) compared to the other two models in the station Ahmadapur and the similar trend is observed in all the stations. Overall, the RF model demonstrates superior performance in predicting groundwater levels during both pre-monsoon and post-monsoon seasons, particularly in highly groundwater-irrigated alluvial aquifers of the southern region.

Reconstruction of Past Water Levels in Data-Deficient Karst Springs

Karst aquifers are crucial for providing fresh water worldwide but are also incredibly sensitive to human impact and climate change. This study aims to reconstruct the historical water levels of karst springs, despite the lack of data. By combining collected data, we have created a detailed numerical model to understand the complex behavior of karst aquifers. Our research reveals significant drops in the water levels at Longtan Spring, mainly due to the overuse of groundwater and inadequate water recharge, which is critical for the success of the Springs Resurgence project. We have also mapped out historical groundwater levels and identified the necessary conditions to get the spring flowing again. The model proved to be reliable during its calibration from 2000 to 2007, with an average Nash–Sutcliffe efficiency coefficient of 0.52 for the monitoring wells. For the period from 1960 to 2019, our model showed a strong correlation coefficient of over 0.97 when compared with data from the GRACE satellite mission, demonstrating its high accuracy. The approach we have taken in this study provides a feasible way to figure out historical water levels in karst springs, which is vital for protecting these essential fresh water sources. This work will provide a strong basis for policies to restore the spring.

Improving a hydrological model by coupling it with an LSTM water use forecasting model

Long short-term memory (LSTM) networks have demonstrated excellent capabilities in identifying nonlinear relationships between variables and have proven to be effective in water use forecast modeling. However, current LSTM water use forecasting models do not consider the influence of regional water resources, which limits their ability to make full use of hydrological data. To address this issue, a distributed hydrological model (WetSpa-WW) was integrated in this study with a deep learning model (LSTM) based on water use activities. The water resources results simulated by WetSpa-WW were considered as an additional training input in the LSTM for water use forecasting. The coupled WetSpa-LSTM model was tested on the upper-middle of the Weihe River Basin, and the simulations were compared with those obtained from an experiment that did not consider water resources factors. The results indicated that the WetSpa-LSTM model performed better than the original model in simulating the streamflow. During calibration, logarithmic form of Nash-Sutcliffe efficiency (NSEln) increased from 0.33 to 0.54 and the relative error (RE) decreased from 28 % to 13 %. Moreover, we observed that water resources factors can improve the accuracy of agricultural water use forecasting (NSE increases by 0.10) more than that of non-agricultural water use forecasting (NSE increases by 0.04). Overall, this study demonstrates the value of considering regional water resources in water use forecasting, and provides a perspective for integrating a distributed hydrological model (WetSpa-WW) and a deep learning model (LSTM) based on water use activities.

IoT-Enabled Embedded Virtual Sensor for Energy-Efficient Scalable Online Water Quality Monitoring System

Pollutants introduced into water sources by industries and individuals have made it difficult to obtain enough clean drinking water in the modern world. This calls for a mechanism that periodically assesses the water's quality. There are portable instruments available to measure the factors impacting water quality. But the user needs solutions that are inexpensive, automated, reliable, and responsive. For monitoring of large water bodies, a low-cost, scalable, edge processing system in real-time is created to satisfy the current needs. It is designed using a Raspberry Pi, a display unit, and standard sensors for variables including temperature, ORP, pH, conductivity, and DO. Edge intelligence along with tiny ML is used to deploy the virtual sensor for estimation of the degree of pollution in water which otherwise requires a costly and tedious process. With an edge reaction time of 2.70ns and a notable Nash-Sutcliffe efficiency coefficient of 0.99 for BOD and COD estimation in water, this model is created utilizing the randomized tree classification. With the use of a power-efficient algorithm, the 8.4w system energy consumption of the created prototype allows it to operate on AC and solar power and can be effective in difficult climatic conditions.

Stage and discharge prediction from documentary time-lapse imagery

Imagery from fixed, ground-based cameras is rich in qualitative and quantitative information that can improve stream discharge monitoring. For instance, time-lapse imagery may be valuable for filling data gaps when sensors fail and/or during lapses in funding for monitoring programs. In this study, we used a large image archive (&gt;40,000 images from 2012 to 2019) from a fixed, ground-based camera that is part of a documentary watershed imaging project (https://plattebasintimelapse.com/). Scalar image features were extracted from daylight images taken at one-hour intervals. The image features were fused with United States Geological Survey stage and discharge data as response variables from the site. Predictions of stage and discharge for simulated year-long data gaps (2015, 2016, and 2017 water years) were generated from Multi-layer Perceptron, Random Forest Regression, and Support Vector Regression models. A Kalman filter was applied to the predictions to remove noise. Error metrics were calculated, including Nash-Sutcliffe Efficiency (NSE) and an alternative threshold-based performance metric that accounted for seasonal runoff. NSE for the year-long gap predictions ranged from 0.63 to 0.90 for discharge and 0.47 to 0.90 for stage, with greater errors in 2016 when stream discharge during the gap period greatly exceeded discharge during the training periods. Importantly, and in contrast to gap-filling methods that do not use imagery, the high discharge conditions in 2016 could be visually (qualitatively) verified from the image data. Half-year test sets were created for 2016 to include higher discharges in the training sets, thus improving model performance. While additional machine learning algorithms and tuning parameters for selected models should be tested further, this study demonstrates the potential value of ground-based time-lapse images for filling large gaps in hydrologic time series data. Cameras dedicated for hydrologic sensing, including nighttime imagery, could further improve results.

The Forecast of Streamflow through Göksu Stream Using Machine Learning and Statistical Methods

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

Combining geospatial information and SCS-CN for surface runoff estimation in Rib watershed, upper Blue Nile Basin, Ethiopia

Surface runoff is the most significant environmental concern in the Rib Watershed. Hence, this study estimates runoff by combining geospatial information and the Soil Conservation Service Curve Number (SCS-CN) model in the watershed. Rainfall, land use, land cover, hydrologic soil group, maximum soil water retention, and CN values were processed using Arc GIS and ERDAS Imagine software accordingly. The model was validated using the coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE). The R 2 (0.9861, 0.9508, and 0.9136) and NSE (0.7, 0.68, and 0.6) values for the periods (2018, 2020, and 2022), respectively, confirmed the good performance of the model. The result also showed runoff ranges from 497 mm/year to 1,258 mm/year. Therefore, the highest runoff is observed in most areas of Farta and Debre Tabor and in some parts of Lay Gayint, Fogera, and Kemkem districts. Consequently, this may result in a loss of soil moisture, a decline of surface and ground water, low crop yield and animal fodder, and unproductivity of the land. This, in turn, affects food security and the livelihoods of the community at large in the region. Therefore, well-planned watershed management practices should be put into practice by prioritizing runoff hotspot sites in the catchment.