# Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin

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## Abstract

**:**

## 1. Introduction

## 2. Materials

#### 2.1. Study Area

^{2}(Figure 1). This river primarily traverses through Nagano and Niigata prefectures before discharging into the Sea of Japan in Niigata. The upper part of the Shinano River Basin (SRB), positioned centrally on the Japanese mainland, is known as the Chikuma River, with a length of 214 km and an area of 7163 km

^{2}, accounting for 58% and 60% of SRB, respectively. Characterized by its alpine terrain, the Chikuma River basin predominantly features a stratum composed of andesite. The climate is the most typical inland climate. The southern region displays predominant climatic influences from the East China Sea, while complex weather conditions govern the northern areas due to its proximity to the Northern Land Region. Varied annual precipitation intensities are observed: roughly 1000–1400 mm in the upper course, around 1000 mm in the middle, and approximately 1400–1800 mm in the lower course. The mid-lower sections of the SRB’s precipitation patterns interrelate with the climatic characteristics of the Sea of Japan, with 40–50% of their annual rainfall largely snowfall, which tends to occur between November and February. The subsequent period of high precipitation transpires amidst the June to July “plum rain” season. The region’s ample water supply and fertile soil enhance its recognition as one of Japan’s premier paddy-producing regions. The Shinano River is a flood-prone river, with 23 recorded floods between 1931 and 1960. At the same time, it is also the primary water source for industry and agriculture in the basin. Therefore, combined with the needs of flood control, irrigation, water supply, and power generation, simulation runoff in SRB is of great interest.

#### 2.2. Data

_{0}), potential evapotranspiration (EP), leaf area vegetation index (LAI), soil and land use data. The DEM was sourced from MERIT-DEM (Multi-Error-Removed Improved Terrain DEM) data product developed by Yamazaki, D. et al. in Japan [50]. The PET with a 0.25° resolution was obtained from the GLEAM [49]. The EP came from the Climate Research Unit (CRU) with a resolution of 0.5° [51]. The LAI was downloaded from the National Environmental Information Center (NCEI) with monthly and 0.05° resolutions [52]. The Food and Agriculture Organization of the United Nations (FAO) provided the soil data [53]. The land use data obtained at a 500 m grid scale was acquired from the USGS Land Cover Research Institute [54]. Considering that such a large basin will cause huge calculation costs, the spatial resolution of the BTOP model was set to 1 km, and all input data were resampled to the same resolution using the nearest neighbor method.

## 3. Methodology

#### 3.1. Evaluation Criteria

^{O}is the observed discharge data, X

^{S}is the simulated discharge data by single BTOP, standalone LSTM and BTOP–LSTM hybrid models, i is the runoff time series, n is the time steps in the evaluation.

#### 3.2. Hydrological Model: The Block-Wise Use of TOPMODEL (BTOP Model)

_{0}c), drying function parameter (α), decay factor of lateral transmissivity (m), block-average saturation deficit (SD

_{bar}), and groundwater dischargeability (D

_{0}). These parameters are automatically calibrated using a shuffled complex evolution optimization algorithm (SCE-UA) developed at the University of Arizona [68]. Except for D

_{0}, all other parameters are provided per block. In terms of output variables, beyond simulating essential values such as discharge, flow generation flux, and evapotranspiration for specific grids, BTOP can output the average values of internal variables upstream of a specified grid. Taking the calculation of net precipitation on a grid as an example, the BTOP model divides each grid into four zones: vegetation, root, unsaturated, and subsurface. For each BTOP grid, the net precipitation calculation occurs in the vegetation zone and is reduced by the canopy interception water storage and actual interception evaporation of the canopy (ET

_{0}). The net precipitation of the basin is the average of the calculated values of all grids within the basin. A detailed listing of these internal variables is presented in Table 1.

#### 3.3. Long Short-Term Memory Network (LSTM)

_{t}, the input gate i

_{t}and the output gate O

_{t}) to determine when a hidden state should be updated or reset through a dedicated mechanism.

_{t}) and the hidden state of the previous time step (h

_{t}

_{−1}), as shown in Figure 2. Firstly, LSTM introduces the concept of candidate memory cell ($\tilde{{C}_{t}}$), and uses tanh as the activation function with a value range of (−1, 1). This leads to the following equation at time step t, where W

_{c}is the weight parameter; b

_{c}is the bias parameter:

_{t}), the output gate determines whether the memory cell should influence the output at the current time step [71]. Three fully connected layers with sigmoid activation functions (σ) compute the values of the input, forget, and output gates. The equations at time step t as follows, where W

_{f}, W

_{i}, W

_{o}are weight parameters, b

_{f}, b

_{i}, b

_{o}are bias parameters:

_{t}

_{−1}) and candidate memory cell state, the current memory cell internal state at time step t could be updated by Equation (9), where ⊙ is Hadamard (elementwise) product operator:

_{t}). The equation at time step t is as follows:

#### 3.4. Feature Dimensionality Reduction Method

#### 3.4.1. Pearson Correlation Coefficient (PCC)

#### 3.4.2. Principal Component Analysis (PCA)

#### 3.5. Model Design

#### 3.5.1. Standalone LSTM Settings

#### 3.5.2. Construction of Hybrid Models

- Hybrid-1: Pearson correlation analysis, a common feature selection method, was employed to screen the feature variables demonstrating high correlation with the observed discharge as additional input to the standalone LSTM.
- Hybrid-2: Employing principal component analysis as a feature dimensionality reduction method, converted eight input features into a few principal components and fed them as additional features into the standalone LSTM.
- Hybrid-3: All output variables from the BTOP model were directly fed into the standalone LSTM as additional features without any dimensionality reduction.
- The hybrid model adopted the same model structure as the standalone LSTM (avoiding overfitting and underfitting), with variations in input variables only. The hybrid models were executed ten times on each sub-basin, and the average value was taken.

## 4. Results and Discussion

#### 4.1. BTOP Model Simulation

^{3}/s, RMSE of 37.54 m

^{3}/s, NSE of 0.77 and PCC of 0.89 for calibration, in addition to 19.01 m

^{3}/s, 31.19 m

^{3}/s, 0.63 and 0.82 for validation, respectively. The poor simulation performance of SRB-5 and SRB-6 could potentially be attributed to the unreasonable sub-basin division within the BTOP model. In addition, since the flood in the lower courses of SRB is largely affected by snowmelt from March to April, the BTOP model significantly underestimated the discharge during flood periods in SRB-5 and SRB-6 due to the lower observed rainfall. Moreover, the Shinano River is the main source of water for industry and agriculture in the basin, and more than half of the population in the basin depends on the Shinano River for water supply. In combination with the needs of flood control, irrigation, water supply and power generation, numerous large comprehensive utilization projects were built in the lower courses of the basin (Niigata has six flood control reservoirs with a total storage capacity of nearly 100 million cubic meters). Therefore, human activities such as reservoir storage and irrigation may also lead to the poor simulation performance of the BTOP model in SRB-5 and SRB-6.

_{sim}). In the natural world, affected by factors such as temperature, humidity, atmospheric pressure, and wind, both precipitation and evapotranspiration exhibit significant seasonal and inter-annual variation characteristics, which were also reflected in BTOP model calculations (Figure 5a–c). As a process-based hydrologic model, the parameters of the BTOP model have clear physical significance and can describe the hydrologic process more accurately by solving continuous and dynamic equations. For instance, after a rainfall event (refer to Figure 5a), there was a significant decrease in soil moisture saturation deficit (Figure 5d). Moreover, since the root zone is located above the unsaturated zone in the BTOP model structure, the decrease in the root zone water storage (Figure 5e) was usually accompanied by a surge in unsaturated zone water storage (Figure 5f). On the other hand, as shown in Figure 5g,i, the comparison between Q

_{oft}and Q

_{sim}confirms that the simulated discharge was primarily determined by flow generation flux. In conclusion, the output variables of the BTOP model, derived via mass and energy equations, potentially offered informative physical data.

#### 4.2. Feature Dimensionality Reduction Results

#### 4.2.1. Feature Selection (PCC)

_{oft}), and groundwater discharge flux (Q

_{v}) output from the BTOP model demonstrated robust correlations with observed discharge (Q

_{obs}) over three different lag times as they reached their peak when the lag time was one day. Effec.P, Q

_{oft}, and Q

_{v}displayed a significant positive correlation with Q

_{obs}, while the SD showed a significant negative correlation with Q

_{obs}. Remarkably, the relationship of actual evaporation (ET), actual interception evapotranspiration (ET

_{0}), root zone water storage (S

_{rz}), and unsaturated zone water storage (S

_{uz}) to Q

_{obs}was somewhat weak but typically peaked at a one-day lag time. Therefore, this study determined the lag time as one day in the Shinano River Basin.

_{oft}, Q

_{v}, and Q

_{sim}variables present high correlations among themselves and a clear negative correlation with SD. Moreover, due to the model design, actual evapotranspiration mainly occurs in the root zone. Therefore, ET calculated by the model showed a significant negative correlation with S

_{rz}. Feature selection not only removes features unrelated to the training target but also removes a redundant feature that provides the same information. In this study, Q

_{obs}was mainly associated with five variables: Effec.P, SD, Q

_{oft}, Q

_{v}and Q

_{sim}. However, the two variables may provide redundant information due to the strong correlation between Effec.P and Q

_{v}(0.91 in all six sub-basins). Therefore, based on the consideration of a simplified model, the input features SD, Q

_{oft}, Q

_{v}, and Q

_{sim}determined by the PCC feature selection method were selected to construct Hybrid-1.

#### 4.2.2. Feature Extraction (PCA)

#### 4.3. Comparison of Performance between Varied Models

^{3}/s to 75.66 m

^{3}/s and 81.41 m

^{3}/s, the RMSE value reduced from 184.85 m

^{3}/s to 167.10 m

^{3}/s and 168.24 m

^{3}/s, the NSE value increased from 0.75 to 0.80 and 0.79, and the PCC value rose from 0.88 to 0.90. These results underlined the superior simulation performance achieved by Hybrid-1 and Hybrid-2, indicating that the output variables of the BTOP model can provide more physical information to the standalone LSTM, thereby improving simulation accuracy.

^{3}/s, the RMSE value increased by 4.86 m

^{3}/s, the NSE value decreased by 0.09, and the PCC value decreased by 0.01 at SRB-1. The results suggested that employing feature selection methods such as PCC and feature extraction methods like PCA can significantly reduce redundant and irrelevant information within hydrological sequences. Filtering the output variables of the BTOP model through two feature dimensionality reduction methods and subsequently incorporating these variables into the input features of the standalone LSTM can effectively improve the performance of runoff simulation.

## 5. Conclusions

- (1)
- The data-driven LSTM demonstrated superior and more stable simulation performance compared to the process-based BTOP model. The BTOP model failed to simulate two downstream basins with NSE of only 0.15 and 0.17. On the contrary, LSTM performed well across the basin, with NSE values all exceeding 0.70. Moreover, the BTOP model significantly underestimated the discharge of the six sub-basins, while the flow duration curve simulated by LSTM fitted well with that of the observed discharge.
- (2)
- Feeding the output variables from the BTOP model into LSTM as input features could provide LSTM with more physical information for learning, thereby improving simulation accuracy. Hybrid-1 and Hybrid-2 displayed comparable performances throughout the basin, both outperforming the standalone LSTM. Moreover, the exceedance probability shows that the fit of the flow duration curve of Hybrid-1 and Hybrid-2 was better than that of the standalone LSTM, and the main error of the two hybrid models originates from the slight underestimation of high flow.
- (3)
- The feature selection method, PCC, and the feature extraction method, PCA, can effectively eliminate noise within the hydrological sequences. Feeding all the BTOP model estimates into LSTM (Hybrid-3) does not enhance simulation performance but instead leads to poor simulation accuracy due to redundant and irrelevant information. Notably, Hybrid-3 exhibited overestimation behavior in the mid-flow, resulting in model accuracy far lower than Hybrid-1 and Hybrid-2 and even lower than the standalone LSTM. It is confirmed that implementing the feature dimension reduction method before constructing the hybrid model is an effective strategy to improve simulation accuracy.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Hirpa, F.A.; Salamon, P.; Alfieri, L.; Thielen-del Pozo, J.; Zsoter, E.; Pappenberger, F. The Effect of Reference Climatology on Global Flood Forecasting. J. Hydrometeorol.
**2016**, 17, 1131–1145. [Google Scholar] [CrossRef] - Croley, T.E.; He, C.S. Distributed-Parameter Large Basin Runoff Model. I: Model Development. J. Hydrol. Eng.
**2005**, 10, 173–181. [Google Scholar] [CrossRef] - Pagano, T.C.; Wood, A.W.; Ramos, M.-H.; Cloke, H.L.; Pappenberger, F.; Clark, M.P.; Cranston, M.; Kavetski, D.; Mathevet, T.; Sorooshian, S.; et al. Challenges of Operational River Forecasting. J. Hydrometeorol.
**2014**, 15, 1692–1707. [Google Scholar] [CrossRef] - Liu, L.; Zhou, L.; Ao, T.; Liu, X.; Shu, X. Flood Hazard Analysis Based on Rainfall Fusion: A Case Study in Dazhou City, China. Remote Sens.
**2022**, 14, 4843. [Google Scholar] [CrossRef] - Shan, Y.; Yan, C.; Liu, J.; Liu, C. Predicting Velocity and Turbulent Kinetic Energy inside an Emergent Phragmites Australis Canopy with Real Morphology. Environ. Fluid Mech.
**2023**, 23, 943–963. [Google Scholar] [CrossRef] - Liu, C.; Shan, Y. Impact of an Emergent Model Vegetation Patch on Flow Adjustment and Velocity. Proc. Inst. Civ. Eng.-Water Manag.
**2022**, 175, 55–66. [Google Scholar] [CrossRef] - Duan, Z.; Ren, Y.; Liu, X.; Lei, H.; Hua, X.; Shu, X.; Zhou, L. A Comprehensive Comparison of Data Fusion Approaches to Multi-Source Precipitation Observations: A Case Study in Sichuan Province, China. Environ. Monit. Assess.
**2022**, 194, 422. [Google Scholar] [CrossRef] - Zhu, Y.; Liu, L.; Qin, F.; Zhou, L.; Zhang, X.; Chen, T.; Li, X.; Ao, T. Application of the Regression-Augmented Regionalization Approach for BTOP Model in Ungauged Basins. Water
**2021**, 13, 2294. [Google Scholar] [CrossRef] - Du, J.; Yu, X.; Zhou, L.; Ren, Y.; Ao, T. Precipitation Characteristics across the Three River Headwaters Region of the Tibetan Plateau: A Comparison between Multiple Datasets. Remote Sens.
**2023**, 15, 2352. [Google Scholar] [CrossRef] - Beven, K. Linking Parameters Across Scales—Subgrid Parameterizations and Scale-Dependent Hydrological Models. Hydrol. Process.
**1995**, 9, 507–525. [Google Scholar] [CrossRef] - Liu, W.; Pan, J.; Ren, Y.; Wu, Z.; Wang, J. Coupling Prediction Model for Long-Term Displacements of Arch Dams Based on Long Short-Term Memory Network. Struct. Control Health Monit.
**2020**, 27, e2548. [Google Scholar] [CrossRef] - Yaseen, Z.M.; El-Shafie, A.; Jaafar, O.; Afan, H.A.; Sayl, M.N. Artificial Intelligence Based Models for Stream-Flow Forecasting: 2000–2015. J. Hydrol.
**2015**, 530, 829–844. [Google Scholar] [CrossRef] - Tian, P.; Lu, H.; Feng, W.; Guan, Y.; Xue, Y. Large Decrease in Streamflow and Sediment Load of Qinghai-Tibetan Plateau Driven by Future Climate Change: A Case Study in Lhasa River Basin. Catena
**2020**, 187, 104340. [Google Scholar] [CrossRef] - Salvadore, E.; Bronders, J.; Batelaan, O. Hydrological Modelling of Urbanized Catchments: A Review and Future Directions. J. Hydrol.
**2015**, 529, 62–81. [Google Scholar] [CrossRef] - Arnone, E.; Zoratti, V.; Formetta, G.; Bosa, S.; Petti, M. Predicting Peakflows in Mountain River Basins and Data-Scarce Areas: A Case Study in Northeastern Italy. Hydrol. Sci. J.
**2023**, 68, 432–447. [Google Scholar] [CrossRef] - Gupta, H.V.; Nearing, G.S. Debates—The Future of Hydrological Sciences: A (Common) Path Forward? Using Models and Data to Learn: A Systems Theoretic Perspective on the Future of Hydrological Science. Water Resour. Res.
**2014**, 50, 5351–5359. [Google Scholar] [CrossRef] - Wagener, T.; Gupta, H.V. Model Identification for Hydrological Forecasting under Uncertainty. Stoch. Environ. Res. Risk Assess.
**2005**, 19, 378–387. [Google Scholar] [CrossRef] - Renard, B.; Kavetski, D.; Kuczera, G.; Thyer, M.; Franks, S.W. Understanding Predictive Uncertainty in Hydrologic Modeling: The Challenge of Identifying Input and Structural Errors. Water Resour. Res.
**2010**, 46, W05521. [Google Scholar] [CrossRef] - Vache, K.B.; McDonnell, J.J. A Process-Based Rejectionist Framework for Evaluating Catchment Runoff Model Structure. Water Resour. Res.
**2006**, 42, W02409. [Google Scholar] [CrossRef] - Clark, M.P.; Nijssen, B.; Lundquist, J.D.; Kavetski, D.; Rupp, D.E.; Woods, R.A.; Freer, J.E.; Gutmann, E.D.; Wood, A.W.; Brekke, L.D.; et al. A Unified Approach for Process-Based Hydrologic Modeling: 1. Modeling Concept. Water Resour. Res.
**2015**, 51, 2498–2514. [Google Scholar] [CrossRef] - Clark, M.P.; Bierkens, M.F.P.; Samaniego, L.; Woods, R.A.; Uijlenhoet, R.; Bennett, K.E.; Pauwels, V.R.N.; Cai, X.; Wood, A.W.; Peters-Lidard, C.D. The Evolution of Process-Based Hydrologic Models: Historical Challenges and the Collective Quest for Physical Realism. Hydrol. Earth Syst. Sci.
**2017**, 21, 3427–3440. [Google Scholar] [CrossRef] [PubMed] - Zhu, S.; Luo, X.; Yuan, X.; Xu, Z. An Improved Long Short-Term Memory Network for Streamflow Forecasting in the Upper Yangtze River. Stoch. Environ. Res. Risk Assess.
**2020**, 34, 1313–1329. [Google Scholar] [CrossRef] - Nearing, G.S.; Kratzert, F.; Sampson, A.K.; Pelissier, C.S.; Klotz, D.; Frame, J.M.; Prieto, C.; Gupta, H.V. What Role Does Hydrological Science Play in the Age of Machine Learning? Water Resour. Res.
**2021**, 57, e2020WR028091. [Google Scholar] [CrossRef] - Feng, D.; Liu, J.; Lawson, K.; Shen, C. Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs Can Approach State-Of-The-Art Hydrologic Prediction Accuracy. Water Resour. Res.
**2022**, 58, e2022WR032404. [Google Scholar] [CrossRef] - Liu, L.; Zhou, L.; Gusyev, M.; Ren, Y. Unravelling and Improving the Potential of Global Discharge Reanalysis Dataset in Streamflow Estimation in Ungauged Basins. J. Clean. Prod.
**2023**, 419, 138282. [Google Scholar] [CrossRef] - Xiao, Q.; Zhou, L.; Xiang, X.; Liu, L.; Liu, X.; Li, X.; Ao, T. Integration of Hydrological Model and Time Series Model for Improving the Runoff Simulation: A Case Study on BTOP Model in Zhou River Basin, China. Appl. Sci.
**2022**, 12, 6883. [Google Scholar] [CrossRef] - Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput.
**1997**, 9, 1735–1780. [Google Scholar] [CrossRef] - Li, Q.; Wang, Z.; Shangguan, W.; Li, L.; Yao, Y.; Yu, F. Improved Daily SMAP Satellite Soil Moisture Prediction over China Using Deep Learning Model with Transfer Learning. J. Hydrol.
**2021**, 600, 126698. [Google Scholar] [CrossRef] - Ahmed, A.A.M.; Deo, R.C.; Feng, Q.; Ghahramani, A.; Raj, N.; Yin, Z.; Yang, L. Hybrid Deep Learning Method for a Week-Ahead Evapotranspiration Forecasting. Stoch. Environ. Res. Risk Assess.
**2022**, 36, 831–849. [Google Scholar] [CrossRef] - Kratzert, F.; Klotz, D.; Brenner, C.; Schulz, K.; Herrnegger, M. Rainfall–Runoff Modelling Using Long Short-Term Memory (LSTM) Networks. Hydrol. Earth Syst. Sci.
**2018**, 22, 6005–6022. [Google Scholar] [CrossRef] - Kratzert, F.; Klotz, D.; Shalev, G.; Klambauer, G.; Hochreiter, S.; Nearing, G. Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine Learning Applied to Large-Sample Datasets. Hydrol. Earth Syst. Sci.
**2019**, 23, 5089–5110. [Google Scholar] [CrossRef] - Tian, Y.; Xu, Y.-P.; Yang, Z.; Wang, G.; Zhu, Q. Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting. Water
**2018**, 10, 1655. [Google Scholar] [CrossRef] - Xiang, Z.; Yan, J.; Demir, I. A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning. Water Resour. Res.
**2020**, 56, e2019WR025326. [Google Scholar] [CrossRef] - Tsai, W.-P.; Feng, D.; Pan, M.; Beck, H.; Lawson, K.; Yang, Y.; Liu, J.; Shen, C. From Calibration to Parameter Learning: Harnessing the Scaling Effects of Big Data in Geoscientific Modeling. Nat. Commun.
**2021**, 12, 5988. [Google Scholar] [CrossRef] - Lu, D.; Konapala, G.; Painter, S.L.; Kao, S.-C.; Gangrade, S. Streamflow Simulation in Data-Scarce Basins Using Bayesian and Physics-Informed Machine Learning Models. J. Hydrometeorol.
**2021**, 22, 1421–1438. [Google Scholar] [CrossRef] - Konapala, G.; Kao, S.-C.; Painter, S.L.; Lu, D. Machine Learning Assisted Hybrid Models Can Improve Streamflow Simulation in Diverse Catchments across the Conterminous US. Environ. Res. Lett.
**2020**, 15, 104022. [Google Scholar] [CrossRef] - Wi, S.; Steinschneider, S. Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change. Water Resour. Res.
**2022**, 58, e2022WR032123. [Google Scholar] [CrossRef] - Saeys, Y.; Inza, I.; Larrañaga, P. A Review of Feature Selection Techniques in Bioinformatics. Bioinformatics
**2007**, 23, 2507–2517. [Google Scholar] [CrossRef] - Zhang, D.; Chen, S.; Zhou, Z.-H. Constraint Score: A New Filter Method for Feature Selection with Pairwise Constraints. Pattern Recognit.
**2008**, 41, 1440–1451. [Google Scholar] [CrossRef] - Wang, J.; Wu, L.; Kong, J.; Li, Y.; Zhang, B. Maximum Weight and Minimum Redundancy: A Novel Framework for Feature Subset Selection. Pattern Recognit.
**2013**, 46, 1616–1627. [Google Scholar] [CrossRef] - Lin, S.-S.; Zhang, N.; Zhou, A.; Shen, S.-L. Time-Series Prediction of Shield Movement Performance during Tunneling Based on Hybrid Model. Tunn. Undergr. Space Technol.
**2022**, 119, 104245. [Google Scholar] [CrossRef] - Pathy, A.; Meher, S.; Balasubramanian, P. Predicting Algal Biochar Yield Using eXtreme Gradient Boosting (XGB) Algorithm of Machine Learning Methods. Algal Res.
**2020**, 50, 102006. [Google Scholar] [CrossRef] - Chen, H.; Chang, X. Photovoltaic Power Prediction of LSTM Model Based on Pearson Feature Selection. Energy Rep.
**2021**, 7, 1047–1054. [Google Scholar] [CrossRef] - Xie, A.; Yang, H.; Chen, J.; Sheng, L.; Zhang, Q. A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network. Atmosphere
**2021**, 12, 651. [Google Scholar] [CrossRef] - Yang, J.; Zhang, D.; Frangi, A.F.; Yang, J. Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans. Pattern Anal. Mach. Intell.
**2004**, 26, 131–137. [Google Scholar] [CrossRef] - Zhang, Y.; Yan, B.; Aasma, M. A Novel Deep Learning Framework: Prediction and Analysis of Financial Time Series Using CEEMD and LSTM. Expert Syst. Appl.
**2020**, 159, 113609. [Google Scholar] [CrossRef] - Xu, W.; Liu, P.; Cheng, L.; Zhou, Y.; Xia, Q.; Gong, Y.; Liu, Y. Multi-Step Wind Speed Prediction by Combining a WRF Simulation and an Error Correction Strategy. Renew. Energy
**2021**, 163, 772–782. [Google Scholar] [CrossRef] - Zhang, J.; Chen, X.; Khan, A.; Zhang, Y.; Kuang, X.; Liang, X.; Taccari, M.L.; Nuttall, J. Daily Runoff Forecasting by Deep Recursive Neural Network. J. Hydrol.
**2021**, 596, 126067. [Google Scholar] [CrossRef] - Martens, B.; Miralles, D.G.; Lievens, H.; van der Schalie, R.; de Jeu, R.A.M.; Fernandez-Prieto, D.; Beck, H.E.; Dorigo, W.A.; Verhoest, N.E.C. GLEAM v3: Satellite-Based Land Evaporation and Root-Zone Soil Moisture. Geosci. Model Dev.
**2017**, 10, 1903–1925. [Google Scholar] [CrossRef] - Yamazaki, D.; Ikeshima, D.; Tawatari, R.; Yamaguchi, T.; O’Loughlin, F.; Neal, J.C.; Sampson, C.C.; Kanae, S.; Bates, P.D. A High-Accuracy Map of Global Terrain Elevations. Geophys. Res. Lett.
**2017**, 44, 5844–5853. [Google Scholar] [CrossRef] - The Potential Evapotranspiration (EP) from the Climatic Research Unit (CRU) of the School of Environmental Sciences (ENV) at the University of East Anglia (UEA). Available online: http://www.cru.uea.ac.uk/ (accessed on 25 March 2022).
- The Leaf Area Index (LAI) of the National Centers for Environmental Information. Available online: https://www.ncei.noaa.gov/Data/Avhrr-Land-Leaf-Area-Index-and-Fapar/Access/ (accessed on 25 March 2022).
- FAO Digital Soil Map of the World (DSMW). Available online: http://www.fao.org/Land-Water/Land/Land-Governance/Landresources-Planning-Toolbox/Category/Details/En/c/1026564/ (accessed on 25 March 2022).
- LP DAAC-MCD12Q1. Available online: https://lpdaac.usgs.gov/Products/Mcd12q1v006/ (accessed on 25 March 2022).
- Takeuchi, K.; Ao, T.Q.; Ishidaira, H. Introduction of Block-Wise Use of TOPMODEL and Muskingum-Cunge Method for the Hydro-Environmental Simulation of a Large Ungauged Basin. Hydrol. Sci. J.-J. Sci. Hydrol.
**1999**, 44, 633–646. [Google Scholar] [CrossRef] - Ao, T.; Ishidaira, H.; Takeuchi, K. Study of Distributed Runoff Simulation Model Based on Block Type Topmodel and Muskingum-Cunge Method. Proc. Hydraul. Eng.
**1999**, 43, 7–12. [Google Scholar] [CrossRef] - Takeuchi, K.; Hapuarachchi, P.; Zhou, M.; Ishidaira, H.; Magome, J. A BTOP Model to Extend TOPMODEL for Distributed Hydrological Simulation of Large Basins. Hydrol. Process.
**2008**, 22, 3236–3251. [Google Scholar] [CrossRef] - Zhou, M.C.; Ishidaira, H.; Hapuarachchi, H.P.; Magome, J.; Kiem, A.S.; Takeuchi, K. Estimating Potential Evapotranspiration Using Shuttleworth-Wallace Model and NOAA-AVHRR NDVI Data to Feed a Distributed Hydrological Model over the Mekong River Basin. J. Hydrol.
**2006**, 327, 151–173. [Google Scholar] [CrossRef] - Beven, K.J.; Kirkby, M.J.; Freer, J.E.; Lamb, R. A History of TOPMODEL. Hydrol. Earth Syst. Sci.
**2021**, 25, 527–549. [Google Scholar] [CrossRef] - Barry, D.; Bajracharya, K. On the Muskingum-Cunge Flood Routing Method. Environ. Int.
**1995**, 21, 485–490. [Google Scholar] [CrossRef] - Navarathinam, K.; Gusyev, M.A.; Hasegawa, A.; Magome, J.; Takeuchi, K. Agricultural Flood and Drought Risk Reduction by a Proposed Multi-Purpose Dam: A Case Study of the Malwathoya River Basin, Sri Lanka. In Proceedings of the 21st International Congress on Modelling and Simulation (MODSIM 2015), Queensland, Australia, 29 November–4 December 2015; Weber, T., McPhee, M.J., Anderssen, R.S., Eds.; Modelling & Simulation Soc Australia & New Zealand Inc.: Christchurch, New Zealand, 2015; pp. 1600–1606. [Google Scholar]
- Ishidaira, H.; Sawada, H.; Masumoto, T. Studies on the Mekong and River Basin-Modelling of Hydrology Water Resources. Hydrol. Process.
**2008**, 22, 1243–1245. [Google Scholar] [CrossRef] - Hapuarachchi, H.A.P.; Takeuchi, K.; Zhou, M.; Kiem, A.S.; Georgievski, M.; Magome, J.; Ishidaira, H. Investigation of the Mekong River Basin Hydrology for 1980-2000 Using the YHyM. Hydrol. Process.
**2008**, 22, 1246–1256. [Google Scholar] [CrossRef] - Liu, L.; Zhou, L.; Li, X.; Chen, T.; Ao, T. Screening and Optimizing the Sensitive Parameters of BTOPMC Model Based on UQ-PyL Software: Case Study of a Flood Event in the Fuji River Basin, Japan. J. Hydrol. Eng.
**2020**, 25, 05020030. [Google Scholar] [CrossRef] - Zhou, L.; Rasmy, M.; Takeuchi, K.; Koike, T.; Selvarajah, H.; Ao, T. Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan. Appl. Sci.
**2021**, 11, 1087. [Google Scholar] [CrossRef] - Zhou, L.; Koike, T.; Takeuchi, K.; Rasmy, M.; Onuma, K.; Ito, H.; Selvarajah, H.; Liu, L.; Li, X.; Ao, T. A Study on Availability of Ground Observations and Its Impacts on Bias Correction of Satellite Precipitation Products and Hydrologic Simulation Efficiency. J. Hydrol.
**2022**, 610, 127595. [Google Scholar] [CrossRef] - Liu, L.; Ao, T.; Zhou, L.; Takeuchi, K.; Gusyev, M.; Zhang, X.; Wang, W.; Ren, Y. Comprehensive Evaluation of Parameter Importance and Optimization Based on the Integrated Sensitivity Analysis System: A Case Study of the BTOP Model in the Upper Min River Basin, China. J. Hydrol.
**2022**, 610, 127819. [Google Scholar] [CrossRef] - Duan, Q.; Sorooshian, S.; Gupta, V. Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models. Water Resour. Res.
**1992**, 28, 1015–1031. [Google Scholar] [CrossRef] - Hochreiter, S.; Schmidhuber, J. LSTM Can Solve Hard Long Time Lag Problems. In Advances in Neural Information Processing Systems 9: Proceedings of the 1996 Conference; Mozer, M.C., Jordan, M.I., Petsche, T., Eds.; Advances in Neural Information Processing Systems; M I T Press: Cambridge, UK, 1997; Volume 9, pp. 473–479. [Google Scholar]
- Yang, S.; Yu, X.; Zhou, Y. LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example. In 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI); IEEE: Shanghai, China, 2020; pp. 98–101. [Google Scholar] [CrossRef]
- Yu, Y.; Si, X.; Hu, C.; Zhang, J. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Comput.
**2019**, 31, 1235–1270. [Google Scholar] [CrossRef] - Zhang, Z.; Qin, H.; Liu, Y.; Wang, Y.; Yao, L.; Li, Q.; Li, J.; Pei, S. Long Short-Term Memory Network Based on Neighborhood Gates for Processing Complex Causality in Wind Speed Prediction. Energy Convers. Manag.
**2019**, 192, 37–51. [Google Scholar] [CrossRef] - Karl Pearson, F.R.S. LIII. On Lines and Planes of Closest Fit to Systems of Points in Space. Lond. Edinb. Dublin Philos. Mag. J. Sci.
**2010**, 2, 559–572. Available online: https://www.tandfonline.com/doi/abs/10.1080/14786440109462720 (accessed on 26 July 2023). [CrossRef] - Hotelling, H. Analysis of a Complex of Statistical Variables into Principal Components. J. Educ. Psychol.
**1933**, 24, 417–441. [Google Scholar] [CrossRef] - Cui, W.; Zhang, Y.; Zhang, X.; Li, L.; Liou, F. Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network. Appl. Sci.
**2020**, 10, 545. [Google Scholar] [CrossRef] - Hood, M.J.; Clausen, J.C.; Warner, G.S. Comparison of Stormwater Lag Times for Low Impact and Traditional Residential Development. J. Am. Water Resour. Assoc.
**2007**, 43, 1036–1046. [Google Scholar] [CrossRef] - Mao, G.; Wang, M.; Liu, J.; Wang, Z.; Wang, K.; Meng, Y.; Zhong, R.; Wang, H.; Li, Y. Comprehensive Comparison of Artificial Neural Networks and Long Short-Term Memory Networks for Rainfall-Runoff Simulation. Phys. Chem. Earth Parts ABC
**2021**, 123, 103026. [Google Scholar] [CrossRef] - Yu, Q.; Jiang, L.; Wang, Y.; Liu, J. Enhancing Streamflow Simulation Using Hybridized Machine Learning Models in a Semi-Arid Basin of the Chinese Loess Plateau. J. Hydrol.
**2023**, 617, 129115. [Google Scholar] [CrossRef] - Lei, H.; Zhao, H.; Ao, T.; Hu, W. Quantifying the Reliability and Uncertainty of Satellite, Reanalysis, and Merged Precipitation Products in Hydrological Simulations over the Topographically Diverse Basin in Southwest China. Remote Sens.
**2022**, 15, 213. [Google Scholar] [CrossRef]

**Figure 2.**Basic structure of LSTM cell. The σ and tanh represent sigmoid and hyperbolic tangent activation functions, respectively. ⊙ is Hadamard (elementwise) product operator. F

_{t}, i

_{t}, and O

_{t}represent the forget gate, update gate, and output gate, respectively. x

_{t}denotes the input data, C

_{t}denotes the cell state, h

_{t}denotes the hidden state, and $\tilde{{C}_{t}}$ denotes the candidate memory cell state.

**Figure 3.**Loss function process of the standalone LSTM at 6 sub-basins. Train_loss: mean-squared error during the training process; val_loss: mean-squared error during the validation process; epochs: iteration steps; loss: mean-squared error. (

**a**) SRB-1; (

**b**) SRB-2; (

**c**) SRB-3; (

**d**) SRB-4; (

**e**) SRB-5; (

**f**) SRB-6.

**Figure 5.**Variables output by BTOP at SRB-1. (

**a**) Is the net precipitation (Effec.P), (

**b**) is the actual evaporation (ET), (

**c**) is the actual interception evapotranspiration (ET

_{0}), (

**d**) is the soil moisture saturation deficit (SD), (

**e**) is the root zone water storage (S

_{rz}), (

**f**) is the unsaturated zone water storage (S

_{uz}), (

**g**) is the flow generation flux (Q

_{oft}), (

**h**) is the groundwater recharge flux (Q

_{v}), (

**i**) is the simulated discharge (Q

_{sim}).

**Figure 6.**Correlation between variables with a lag time of one day. EFFEC.P: the net precipitation, ET: the actual evaporation, ET

_{0}: the actual interception evapotranspiration, SD: the soil moisture saturation deficit, S

_{rz}: the root zone water storage, S

_{uz}: the unsaturated zone water storage, Q

_{oft}: the flow generation flux, Q

_{v}: the groundwater recharge flux, Q

_{sim}: the simulated discharge, Q

_{obs}: the observed discharge. (

**a**) SRB-1, (

**b**) SRB-2, (

**c**) SRB-3, (

**d**) SRB-4, (

**e**) SRB-5, (

**f**) SRB-6.

**Figure 8.**Comparison of runoff simulation at six sub-basins by BTOP, Standalone LSTM, Hybrid-1, Hybrid-2, and Hybrid-3.

**Figure 9.**Comparison of the exceedance probabilities of the observed discharge and simulated discharge from varied models at six sub-basins. (

**a**) SRB-1, (

**b**) SRB-2, (

**c**) SRB-3, (

**d**) SRB-4, (

**e**) SRB-5, (

**f**) SRB-6.

Variable | Unit | Description |
---|---|---|

Effec.P | mm | Net precipitation |

ET | mm | Actual evaporation |

ET_{0} | mm | Actual interception evapotranspiration |

SD | mm | The soil moisture saturation deficit |

S_{rz} | mm | The root zone water storage |

S_{uz} | mm | The unsaturated zone water storage |

Q_{oft} | mm | Flow generation flux, the sum of simulated Hortonian overland flow flux, saturation excess runoff flux and groundwater flux |

Q_{v} | mm | Groundwater recharge flux |

Sub- Basin | Calibration Period | Validation Period | ||||||
---|---|---|---|---|---|---|---|---|

MAE (m^{3}/s) | RMSE (m^{3}/s) | NSE | PCC | MAE (m^{3}/s) | RMSE (m^{3}/s) | NSE | PCC | |

SRB-1 | 19.62 | 37.54 | 0.77 | 0.89 | 19.01 | 31.19 | 0.63 | 0.82 |

SRB-2 | 37.15 | 64.72 | 0.56 | 0.81 | 33.79 | 53.07 | 0.66 | 0.88 |

SRB-3 | 63.79 | 103.09 | 0.70 | 0.89 | 62.34 | 94.72 | 0.66 | 0.89 |

SRB-4 | 125.31 | 155.40 | 0.65 | 0.85 | 125.34 | 155.81 | 0.55 | 0.82 |

SRB-5 | 205.73 | 324.06 | 0.14 | 0.54 | 199.18 | 340.60 | 0.15 | 0.59 |

SRB-6 | 210.34 | 338.52 | 0.19 | 0.54 | 192.25 | 333.84 | 0.17 | 0.59 |

**Table 3.**Correlation coefficient between output variables of BTOP model and observed discharge under different lag times (* indicates that the variable is related to observed discharge with a p-value of less than 5%, ** indicates that the variable is related to observed discharge with a p-value of less than 1%).

Sub-Basin | Lag Time (day) | Effec.P | ET | ET_{0} | SD | S_{rz} | S_{uz} | Q_{oft} | Q_{v} |
---|---|---|---|---|---|---|---|---|---|

SRB-1 | 0 | 0.43 ** | 0.19 ** | 0.19 ** | −0.58 ** | 0.17 ** | 0.43 ** | 0.71 ** | 0.60 ** |

1 | 0.69 ** | 0.18 ** | 0.28 ** | −0.58 ** | 0.17 ** | 0.47 ** | 0.78 ** | 0.80 ** | |

2 | 0.37 ** | 0.16 ** | 0.20 ** | −0.38 ** | 0.11 ** | 0.34 ** | 0.37 ** | 0.39 ** | |

SRB-2 | 0 | 0.41 ** | 0.35 ** | 0.24 ** | −0.68 ** | 0.05 * | −0.11 * | 0.72 ** | 0.54 ** |

1 | 0.55 ** | 0.35 ** | 0.30 ** | −0.67 ** | 0.04 * | 0.11 ** | 0.76 ** | 0.67 ** | |

2 | 0.35 ** | 0.22 ** | 0.33 ** | −0.54 ** | 0.01 | −0.12 ** | 0.51 ** | 0.42 ** | |

SRB-3 | 0 | 0.36 ** | 0.28 ** | 0.20 ** | −0.64 ** | 0.14 ** | 0.04 | 0.67 ** | 0.51 ** |

1 | 0. 69 ** | 0.28 ** | 0.33 ** | −0.67 ** | 0.14 ** | 0.04 * | 0.86 ** | 0.82 ** | |

2 | 0.41 ** | 0.26 ** | 0.25 ** | −0.49 ** | 0.09 ** | 0.02 | 0.45 ** | 0.46 ** | |

SRB-4 | 0 | 0.24 ** | 0.22 ** | 0.17 ** | −0.55 ** | 0.15 ** | 0.03 | 0.57 ** | 0.41 ** |

1 | 0.66 ** | 0.22 ** | 0.31 ** | −0.62 ** | 0.16 ** | 0.04 | 0.85 ** | 0.78 ** | |

2 | 0.43 ** | 0.20 ** | 0.26 ** | −0.45 ** | 0.11 ** | 0.01 | 0.55 ** | 0.50 ** | |

SRB-5 | 0 | 0.20 ** | 0.18 ** | 0.13 ** | −0.23 ** | 0.16 ** | −0.01 | 0.36 ** | 0.31 ** |

1 | 0.51 ** | 0.17 ** | 0.25 ** | −0.25 ** | 0.16 ** | −0.01 | 0.56 ** | 0.56 ** | |

2 | 0.30 ** | 0.15 ** | 0.20 ** | −0.15 ** | 0.12 ** | −0.04 | 0.32 ** | 0.32 ** | |

SRB-6 | 0 | 0.19 ** | 0.12 ** | 0.11 ** | −0.25 ** | 0.19 ** | 0.02 | 0.36 ** | 0.30 ** |

1 | 0.50 ** | 0.11 ** | 0.23 ** | −0.27 ** | 0.19 ** | 0.03 | 0.56 ** | 0.56 * | |

2 | 0.30 ** | 0.09 ** | 0.18 ** | −0.18 ** | 0.15 ** | −0.01 | 0.34 ** | 0.33 ** |

**Table 4.**The eigenvalues and cumulative variance contribution rates of principal components of 6 sub-basins.

SRB-1 | SRB-2 | SRB-3 | ||||

Ingredient | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) |

1 | 4.207 | 46.740 | 4.146 | 46.063 | 4.059 | 45.102 |

2 | 1.395 | 62.240 | 1.534 | 63.107 | 1.485 | 61.598 |

3 | 1.138 | 74.882 | 1.102 | 75.353 | 1.122 | 74.068 |

4 | 1.002 | 83.873 | 1.001 | 86.445 | 1.003 | 85.143 |

5 | 0.560 | 91.172 | 0.596 | 93.102 | 0.621 | 92.048 |

6 | 0.297 | 94.773 | 0.378 | 97.303 | 0.407 | 96.635 |

7 | 0.227 | 98.069 | 0.169 | 99.178 | 0.190 | 98.743 |

8 | 0.130 | 99.516 | 0.049 | 99.720 | 0.077 | 99.604 |

9 | 0.044 | 100.000 | 0.025 | 100.000 | 0.036 | 100.000 |

SRB-4 | SRB-5 | SRB-6 | ||||

Ingredient | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) | Eigenvalues | Cumulative (%) |

1 | 3.918 | 43.537 | 3.413 | 42.368 | 3.487 | 42.078 |

2 | 1.592 | 61.222 | 1.955 | 64.086 | 2.005 | 64.352 |

3 | 1.139 | 73.879 | 1.672 | 75.085 | 1.554 | 75.361 |

4 | 1.007 | 84.885 | 1.004 | 85.857 | 1.072 | 85.897 |

5 | 0.605 | 91.798 | 0.416 | 92.239 | 0.381 | 92.285 |

6 | 0.395 | 96.184 | 0.236 | 95.905 | 0.235 | 95.916 |

7 | 0.224 | 98.674 | 0.172 | 98.531 | 0.133 | 98.528 |

8 | 0.084 | 99.603 | 0.092 | 99.549 | 0.092 | 99.546 |

9 | 0.036 | 100.000 | 0.041 | 100.000 | 0.041 | 100.000 |

Sub-Basin Number | Metrics | BTOP | Standalone LSTM | Hybrid-1 | Hybrid-2 | Hybrid-3 |
---|---|---|---|---|---|---|

SRB-1 | MAE (m ^{3}/s) | 18.95 | 10.50 | 11.96 | 10.50 | 15.06 |

RMSE (m ^{3}/s) | 31.43 | 26.92 | 25.95 | 25.01 | 31.78 | |

NSE | 0.63 | 0.73 | 0.75 | 0.76 | 0.64 | |

PCC | 0.83 | 0.86 | 0.88 | 0.89 | 0.85 | |

SRB-2 | MAE (m ^{3}/s) | 34.59 | 18.80 | 18.38 | 19.17 | 26.01 |

RMSE (m ^{3}/s) | 53.82 | 46.02 | 40.80 | 41.62 | 45.52 | |

NSE | 0.66 | 0.75 | 0.80 | 0.80 | 0.76 | |

PCC | 0.88 | 0.87 | 0.90 | 0.90 | 0.89 | |

SRB-3 | MAE (m ^{3}/s) | 63.29 | 32.75 | 29.75 | 36.74 | 36.23 |

RMSE (m ^{3}/s) | 95.95 | 68.19 | 61.84 | 61.07 | 74.19 | |

NSE | 0.66 | 0.83 | 0.86 | 0.86 | 0.80 | |

PCC | 0.90 | 0.92 | 0.93 | 0.94 | 0.90 | |

SRB-4 | MAE (m ^{3}/s) | 126.09 | 48.51 | 37.82 | 44.17 | 54.02 |

RMSE (m ^{3}/s) | 157.14 | 77.96 | 69.60 | 72.58 | 84.39 | |

NSE | 0.55 | 0.89 | 0.91 | 0.90 | 0.87 | |

PCC | 0.81 | 0.95 | 0.96 | 0.95 | 0.94 | |

SRB-5 | MAE (m ^{3}/s) | 203.60 | 71.50 | 67.43 | 73.54 | 103.70 |

RMSE (m ^{3}/s) | 345.39 | 157.43 | 152.89 | 151.74 | 165.59 | |

NSE | 0.15 | 0.82 | 0.83 | 0.84 | 0.80 | |

PCC | 0.62 | 0.92 | 0.92 | 0.92 | 0.91 | |

SRB-6 | MAE (m ^{3}/s) | 196.34 | 81.87 | 75.66 | 81.41 | 112.22 |

RMSE (m ^{3}/s) | 338.47 | 184.85 | 167.10 | 168.24 | 196.35 | |

NSE | 0.17 | 0.75 | 0.80 | 0.79 | 0.72 | |

PCC | 0.62 | 0.88 | 0.90 | 0.90 | 0.88 |

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**MDPI and ACS Style**

Nimai, S.; Ren, Y.; Ao, T.; Zhou, L.; Liang, H.; Cui, Y.
Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin. *Water* **2023**, *15*, 3758.
https://doi.org/10.3390/w15213758

**AMA Style**

Nimai S, Ren Y, Ao T, Zhou L, Liang H, Cui Y.
Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin. *Water*. 2023; 15(21):3758.
https://doi.org/10.3390/w15213758

**Chicago/Turabian Style**

Nimai, Silang, Yufeng Ren, Tianqi Ao, Li Zhou, Hanxu Liang, and Yanmin Cui.
2023. "Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin" *Water* 15, no. 21: 3758.
https://doi.org/10.3390/w15213758