# Impact of training dataset size and its hydrometeorological typology on LSTM performance for Rainfall-Runoff modeling: A case study of the Severn River (UK)

Nadia Skifa, Fadil Boodoo, Carole Delenne, Renaud Hostache, Morgan Abily

<figure><img src="image_1.png" /></figure>

## To cite this version:

Nadia Skifa, Fadil Boodoo, Carole Delenne, Renaud Hostache, Morgan Abily. Impact of training dataset size and its hydrometeorological typology on LSTM performance for Rainfall-Runoff modeling: A case study of the Severn River (UK). SimHydro 2023: New modelling paradigms for water issues?, Nov 2023, Chatou/Paris, France. hal-05506931v2

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Submitted on 16 Feb 2026

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# Impact of training dataset size and its hydrometeorological typology on LSTM performance for Rainfall-Runoff modeling : A case study of the Severn River (UK)

Nadia Skifa, Fadil Boodoo, Carole Delenne, Renaud Hostache, Morgan Abily

<figure><img src="image_3.png" /></figure>

UNIVERSITÉ
CÔTE D'AZUR

<figure><img src="image_4.png" /></figure>

Institut de Recherche
pour le Développement
FRANCE

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

UNIVERSITÉ DE
MONTPELLIER

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

International Association
for Hydro-Environment
Engineering and Research

Hosted by
Spain Water and IWH, China
## Context:

* Rainfall-runoff modeling:

* Physically-based models

* high accurate result

* require a detailed catchment characteristics

* high computation cost

* Conceptual models:

* faster result with satisfying performances

* requires more calibration data

* Artificial Neural Network (ANN)

* As fast and more accurate than Conceptual models
Objectives:

* To compare LSTM model with the Superflex model for streamflow prediction.

* To assess the impact of the data size and dataset typology on model performance.
Saxons Lode Gauging station,
Riverstream, Severn Basin

<figure><img src="image_9.png" /></figure>

Summary of catchment hydro-
meteorological time series available in
CAMEL-GB

* Daily from 1 Oct 1970 to 30 Sept 2015

* Precipitation, Potential evapotranspiration, Potential evapotranspiration corrected, Temperature, Wind speed, Specific humidity, Downward shortwave radiation, Long-wave radiation, Specific discharge, Discharge

* Data source : CEH-GEAR, CHESS-PE, CHESS-met, UK National River Flow
## Recurrent Neural Network structure

<figure><img src="image_10.png" /></figure>

## Structure of a LSTM Cell

<figure><img src="image_11.png" /></figure>

## Superflex Scheme

<figure><img src="image_12.png" /></figure>

* R : Rainfall

* S<sub>UR</sub> : Unsaturated Reservoir Storage

* S<sub>SR</sub> : Slow Reservoir Storage

* S<sub>FR</sub> : Fast Reservoir Storage

* Q<sub>T</sub> : Total Run-off
<figure><img src="image_13.png" /></figure>

Evaluation Metric :

$$ \text{Nash-Sutcliffe Efficiency} = 1 - \frac{\sum_{t=1}^{T} (Q_{\text{sim}}^{t} - Q_{\text{obs}}^{t})^2}{\sum_{t=1}^{T} (Q_{\text{obs}}^{t} - \overline{Q}_{\text{obs}})^2} $$
Results : Impact of training data size

SimHydro 2023

New modelling paradigms for water issues?

7TH International
Conference
8TH–10TH November
EDF Lab - CHATOU, FRANCE

LSTM

<table><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>Validation year</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>1976</td><td>0.54</td><td>0.33</td><td>0.012</td><td>0.094</td><td>0.5</td><td>0.25</td><td>0.27</td><td>0.12</td><td>0.37</td><td>0.21</td></tr><tr><td>1977</td><td>0.26</td><td>0.44</td><td>0.38</td><td>0.41</td><td>0.54</td><td>0.15</td><td>0.36</td><td>0.37</td><td>0.71</td><td>0.85</td></tr><tr><td>1978</td><td>0.26</td><td>0.58</td><td>0.33</td><td>0.23</td><td>0.59</td><td>0.5</td><td>0.75</td><td>0.25</td><td>0.61</td><td>0.72</td></tr><tr><td>1979</td><td>0.31</td><td>0.28</td><td>0.27</td><td>0.16</td><td>0.25</td><td>0.097</td><td>0.25</td><td>0.12</td><td>0.59</td><td>0.8</td></tr><tr><td>1980</td><td>0.57</td><td>0.56</td><td>0.41</td><td>0.56</td><td>0.7</td><td>0.56</td><td>0.54</td><td>0.45</td><td>0.76</td><td>0.91</td></tr><tr><td>1981</td><td>0.52</td><td>0.53</td><td>0.35</td><td>0.55</td><td>0.53</td><td>0.42</td><td>0.49</td><td>0.42</td><td>0.76</td><td>0.74</td></tr><tr><td>1982</td><td>0.34</td><td>0.53</td><td>0.45</td><td>0.55</td><td>0.75</td><td>0.2</td><td>0.38</td><td>0.49</td><td>0.74</td><td>0.94</td></tr><tr><td>1983</td><td>0.42</td><td>0.64</td><td>0.35</td><td>0.38</td><td>0.48</td><td>0.17</td><td>0.48</td><td>0.34</td><td>0.64</td><td>0.78</td></tr><tr><td>1984</td><td>0.47</td><td>0.56</td><td>0.27</td><td>0.4</td><td>0.92</td><td>0.4</td><td>0.26</td><td>0.26</td><td>0.71</td><td>0.77</td></tr><tr><td>1985</td><td>0.36</td><td>0.67</td><td>0.49</td><td>0.46</td><td>0.69</td><td>0.53</td><td>0.57</td><td>0.37</td><td>0.68</td><td>0.7</td></tr><tr><td>1986</td><td>0.18</td><td>0.68</td><td>0.44</td><td>0.43</td><td>0.71</td><td>0.37</td><td>0.44</td><td>0.38</td><td>0.67</td><td>0.76</td></tr><tr><td>1987</td><td>0.33</td><td>0.66</td><td>0.4</td><td>0.37</td><td>0.73</td><td>0.18</td><td>0.041</td><td>0.2</td><td>0.74</td><td>0.7</td></tr><tr><td>1988</td><td>0.44</td><td>0.43</td><td>0.36</td><td>0.48</td><td>0.55</td><td>0.55</td><td>0.56</td><td>0.22</td><td>0.61</td><td>0.76</td></tr><tr><td>1989</td><td>0.44</td><td>0.61</td><td>0.24</td><td>0.25</td><td>0.59</td><td>0.33</td><td>0.57</td><td>0.24</td><td>0.61</td><td>0.66</td></tr><tr><td>1990</td><td>0.36</td><td>-0.07</td><td>0.12</td><td>0.11</td><td>-0.013</td><td>-0.55</td><td>-0.12</td><td>-0.11</td><td>0.43</td><td>0.72</td></tr><tr><td>1991</td><td>0.19</td><td>0.56</td><td>0.31</td><td>0.27</td><td>0.61</td><td>0.38</td><td>0.69</td><td>0.14</td><td>0.7</td><td>0.79</td></tr><tr><td>1992</td><td>0.35</td><td>0.7</td><td>0.35</td><td>0.41</td><td>0.67</td><td>-0.044</td><td>0.068</td><td>0.34</td><td>0.43</td><td>0.61</td></tr><tr><td>1993</td><td>0.52</td><td>0.65</td><td>0.41</td><td>0.4</td><td>0.67</td><td>0.37</td><td>0.39</td><td>0.57</td><td>0.67</td><td>0.7</td></tr><tr><td>1994</td><td>0.3</td><td>0.48</td><td>0.42</td><td>0.5</td><td>0.66</td><td>0.27</td><td>0.35</td><td>0.4</td><td>0.76</td><td>0.84</td></tr><tr><td>1995</td><td>0.41</td><td>-0.039</td><td>0.18</td><td>0.052</td><td>0.14</td><td>0.24</td><td>0.47</td><td>-0.093</td><td>0.46</td><td>0.76</td></tr><tr><td>1996</td><td>0.58</td><td>0.61</td><td>0.19</td><td>0.23</td><td>0.55</td><td>0.49</td><td>0.76</td><td>0.19</td><td>0.56</td><td>0.53</td></tr><tr><td>1997</td><td>0.59</td><td>0.73</td><td>0.35</td><td>0.32</td><td>0.64</td><td>0.13</td><td>0.49</td><td>0.32</td><td>0.51</td><td>0.51</td></tr><tr><td>1998</td><td>0.49</td><td>0.65</td><td>0.38</td><td>0.54</td><td>0.76</td><td>0.12</td><td>0.14</td><td>0.49</td><td>0.7</td><td>0.79</td></tr><tr><td>1999</td><td>0.17</td><td>0.44</td><td>0.52</td><td>0.64</td><td>0.63</td><td>0.27</td><td>0.27</td><td>0.55</td><td>0.7</td><td>0.79</td></tr><tr><td>2000</td><td>0.29</td><td>0.42</td><td>0.25</td><td>0.39</td><td>0.62</td><td>-0.34</td><td>-0.44</td><td>0.43</td><td>0.45</td><td>0.58</td></tr><tr><td>2001</td><td>0.29</td><td>0.63</td><td>0.41</td><td>0.47</td><td>0.64</td><td>0.57</td><td>0.54</td><td>0.34</td><td>0.77</td><td>0.77</td></tr><tr><td>2002</td><td>0.48</td><td>0.69</td><td>0.31</td><td>0.4</td><td>0.78</td><td>0.22</td><td>0.29</td><td>0.29</td><td>0.69</td><td>0.78</td></tr><tr><td>2003</td><td>0.15</td><td>0.35</td><td>0.15</td><td>0.049</td><td>0.26</td><td>0.41</td><td>0.76</td><td>0.006</td><td>0.37</td><td>0.51</td></tr><tr><td>2004</td><td>0.65</td><td>0.71</td><td>0.34</td><td>0.54</td><td>0.78</td><td>0.24</td><td>0.41</td><td>0.39</td><td>0.68</td><td>0.75</td></tr></tbody></table>

1-training year

<figure><img src="image_14.png" /></figure>

Superflex

<table><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>Validation year</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>1976</td><td>0.79</td><td>0.38</td><td>0.88</td><td>0.87</td><td>0.31</td><td>0.66</td><td>0.74</td><td>0.9</td><td>0.83</td><td>0.89</td></tr><tr><td>1977</td><td>0.88</td><td>0.32</td><td>0.88</td><td>0.88</td><td>0.9</td><td>0.72</td><td>0.89</td><td>0.9</td><td>0.86</td><td>0.92</td></tr><tr><td>1978</td><td>0.87</td><td>0.89</td><td>0.9</td><td>0.89</td><td>0.94</td><td>0.71</td><td>0.83</td><td>0.87</td><td>0.87</td><td>0.94</td></tr><tr><td>1979</td><td>0.87</td><td>0.91</td><td>0.88</td><td>0.88</td><td>0.91</td><td>0.73</td><td>0.87</td><td>0.89</td><td>0.88</td><td>0.92</td></tr><tr><td>1980</td><td>0.84</td><td>0.87</td><td>0.88</td><td>0.87</td><td>0.92</td><td>0.74</td><td>0.9</td><td>0.89</td><td>0.83</td><td>0.91</td></tr><tr><td>1981</td><td>0.87</td><td>0.92</td><td>0.87</td><td>0.86</td><td>0.88</td><td>0.72</td><td>0.86</td><td>0.89</td><td>0.87</td><td>0.91</td></tr><tr><td>1982</td><td>0.89</td><td>0.92</td><td>0.88</td><td>0.87</td><td>0.92</td><td>0.73</td><td>0.87</td><td>0.86</td><td>0.9</td><td>0.94</td></tr><tr><td>1983</td><td>0.86</td><td>0.91</td><td>0.9</td><td>0.86</td><td>0.15</td><td>0.71</td><td>0.78</td><td>0.9</td><td>0.87</td><td>0.92</td></tr><tr><td>1984</td><td>0.85</td><td>0.92</td><td>0.89</td><td>0.86</td><td>0.83</td><td>0.71</td><td>0.8</td><td>0.9</td><td>0.86</td><td>0.91</td></tr><tr><td>1985</td><td>0.88</td><td>0.89</td><td>0.89</td><td>0.89</td><td>0.91</td><td>0.95</td><td>0.73</td><td>0.85</td><td>0.92</td><td>0.86</td></tr><tr><td>1986</td><td>0.85</td><td>0.89</td><td>0.87</td><td>0.88</td><td>0.91</td><td>0.7</td><td>0.9</td><td>0.89</td><td>0.86</td><td>0.91</td></tr><tr><td>1987</td><td>0.89</td><td>0.9</td><td>0.89</td><td>0.89</td><td>0.93</td><td>0.78</td><td>0.87</td><td>0.88</td><td>0.87</td><td>0.95</td></tr><tr><td>1988</td><td>0.88</td><td>0.89</td><td>0.87</td><td>0.89</td><td>0.94</td><td>0.73</td><td>0.15</td><td>0.86</td><td>0.89</td><td>0.95</td></tr><tr><td>1989</td><td>0.82</td><td>0.89</td><td>0.89</td><td>0.87</td><td>0.81</td><td>0.68</td><td>0.77</td><td>0.9</td><td>0.85</td><td>0.9</td></tr><tr><td>1990</td><td>0.69</td><td>0.8</td><td>0.87</td><td>0.83</td><td>0.76</td><td>0.62</td><td>0.64</td><td>0.86</td><td>0.83</td><td>0.88</td></tr><tr><td>1991</td><td>0.86</td><td>0.91</td><td>0.88</td><td>0.86</td><td>0.84</td><td>0.71</td><td>0.83</td><td>0.89</td><td>0.87</td><td>0.91</td></tr><tr><td>1992</td><td>0.69</td><td>0.8</td><td>0.88</td><td>0.87</td><td>0.8</td><td>0.67</td><td>0.64</td><td>0.89</td><td>0.8</td><td>0.89</td></tr><tr><td>1993</td><td>0.88</td><td>0.89</td><td>0.91</td><td>0.9</td><td>0.93</td><td>0.69</td><td>0.82</td><td>0.9</td><td>0.88</td><td>0.94</td></tr><tr><td>1994</td><td>0.83</td><td>0.87</td><td>0.89</td><td>0.84</td><td>0.8</td><td>0.69</td><td>0.74</td><td>0.87</td><td>0.87</td><td>0.92</td></tr><tr><td>1995</td><td>0.87</td><td>0.91</td><td>0.89</td><td>0.87</td><td>0.85</td><td>0.71</td><td>0.8</td><td>0.9</td><td>0.86</td><td>0.91</td></tr><tr><td>1996</td><td>0.9</td><td>0.92</td><td>0.9</td><td>0.88</td><td>0.89</td><td>0.71</td><td>0.83</td><td>0.89</td><td>0.89</td><td>0.93</td></tr><tr><td>1997</td><td>0.81</td><td>0.87</td><td>0.88</td><td>0.83</td><td>0.79</td><td>0.71</td><td>0.73</td><td>0.89</td><td>0.83</td><td>0.91</td></tr><tr><td>1998</td><td>0.87</td><td>0.89</td><td>0.87</td><td>0.87</td><td>0.85</td><td>0.66</td><td>0.79</td><td>0.89</td><td>0.85</td><td>0.9</td></tr><tr><td>1999</td><td>0.85</td><td>0.89</td><td>0.86</td><td>0.87</td><td>0.82</td><td>0.67</td><td>0.8</td><td>0.9</td><td>0.83</td><td>0.89</td></tr><tr><td>2000</td><td>0.82</td><td>0.92</td><td>0.87</td><td>0.81</td><td>0.81</td><td>0.67</td><td>0.68</td><td>0.82</td><td>0.86</td><td>0.91</td></tr><tr><td>2001</td><td>0.8</td><td>0.85</td><td>0.88</td><td>0.86</td><td>0.9</td><td>0.67</td><td>0.76</td><td>0.87</td><td>0.88</td><td>0.92</td></tr><tr><td>2002</td><td>0.83</td><td>0.87</td><td>0.87</td><td>0.83</td><td>0.8</td><td>0.71</td><td>0.72</td><td>0.87</td><td>0.85</td><td>0.93</td></tr><tr><td>2003</td><td>0.88</td><td>0.93</td><td>0.88</td><td>0.85</td><td>0.87</td><td>0.74</td><td>0.84</td><td>0.87</td><td>0.9</td><td>0.84</td></tr><tr><td>2004</td><td>0.85</td><td>0.89</td><td>0.89</td><td>0.86</td><td>0.83</td><td>0.7</td><td>0.77</td><td>0.89</td><td>0.84</td><td>0.9</td></tr></tbody></table>

<figure><img src="image_15.png" /></figure>

* Superflex shows better average performance than LSTM

* Superflex is more homogenous than LSTM
Average NSE according to validation year

<figure><img src="image_16.png" /></figure>

* 1-year training: Superflex > LSTM

* As the number of training years extends:

* Superflex NSE remains stable

* LSTM NSE increases

* 6-year training: LSTM start overperforming Superflex
Average NSE according to validation year

<figure><img src="image_17.png" /></figure>

* LSTM:

* From 1 to 6 years: significant reduction in the standard deviation

* Beyond 9 years:

* the standard deviation stabilizes

* The performance (NSE Median) reaches a plateau

* Superflex: Stable

=>LSTM more sensitive to data size. How can we reduce this sensitivity ?
<figure><img src="image_18.png" /><figcaption>K-Means Clustering on annual mean and standard deviation of daily rainfall</figcaption></figure>
3 training year - the same cluster

<table><thead><tr><th></th><th>dryer years</th><th>standard years</th><th>wetter years</th></tr></thead><tbody><tr><td colspan="4">Validation year</td></tr><tr><td>1978-1996-2003</td><td>0.47</td><td>0.73</td><td>0.7</td><td>0.75</td><td>0.79</td><td>0.77</td><td>0.83</td><td>0.43</td><td>0.36</td><td>0.44</td></tr><tr><td>1978-1996-2010</td><td>0.87</td><td></td><td>0.76</td><td>0.81</td><td>0.81</td><td>0.61</td><td>0.83</td><td>0.61</td><td>0.55</td><td>0.56</td></tr><tr><td>1978-1996-2011</td><td></td><td></td><td>0.71</td><td>0.68</td><td>0.75</td><td>0.74</td><td>0.77</td><td>0.83</td><td>0.46</td><td>0.44</td><td>0.46</td></tr><tr><td>1978-2003-2010</td><td>0.85</td><td></td><td>0.74</td><td>0.81</td><td>0.8</td><td>0.81</td><td>0.84</td><td>0.6</td><td>0.48</td><td>0.54</td></tr><tr><td>1978-2003-2011</td><td></td><td></td><td>0.73</td><td>0.5</td><td>0.75</td><td>0.73</td><td>0.72</td><td>0.37</td><td>0.4</td><td>0.38</td></tr><tr><td>1978-2010-2011</td><td></td><td></td><td></td><td>0.75</td><td>0.81</td><td>0.77</td><td>0.81</td><td>0.83</td><td>0.62</td><td>0.57</td><td>0.6</td></tr><tr><td>1996-2003-2010</td><td>0.84</td><td></td><td>0.76</td><td>0.73</td><td>0.74</td><td>0.71</td><td>0.75</td><td>0.57</td><td>0.44</td><td>0.51</td></tr><tr><td>1996-2003-2011</td><td></td><td></td><td>0.72</td><td>0.09</td><td>0.69</td><td>0.71</td><td>0.71</td><td>0.72</td><td>0.37</td><td>0.37</td><td>0.35</td></tr><tr><td>1996-2010-2011</td><td></td><td></td><td>0.74</td><td>0.69</td><td>0.68</td><td>0.7</td><td>0.71</td><td>0.55</td><td>0.52</td><td>0.5</td></tr><tr><td>2003-2010-2011</td><td></td><td></td><td>0.7</td><td>0.71</td><td>0.72</td><td>0.75</td><td>0.70</td><td>0.55</td><td>0.48</td><td>0.49</td></tr><tr><td>1976-1977-1979</td><td>0.78</td><td>0.42</td><td>0.82</td><td>0.85</td><td>0.83</td><td>0.89</td><td>0.62</td><td>0.56</td><td>0.69</td></tr><tr><td>1976-1977-1980</td><td>0.84</td><td></td><td>0.8</td><td>0.88</td><td>0.84</td><td>0.89</td><td>0.9</td><td>0.73</td><td>0.7</td><td>0.78</td></tr><tr><td>1976-1977-1981</td><td>0.84</td><td>0.76</td><td>0.82</td><td>0.85</td><td>0.86</td><td>0.92</td><td>0.87</td><td>0.64</td><td>0.77</td><td>0.77</td></tr><tr><td>1976-1977-1982</td><td>0.78</td><td>0.79</td><td>0.82</td><td>0.89</td><td>0.88</td><td>0.91</td><td>0.92</td><td>0.71</td><td>0.67</td><td>0.75</td></tr><tr><td>1976-1977-1983</td><td>0.86</td><td>0.74</td><td>0.84</td><td>0.86</td><td>0.88</td><td>0.88</td><td>0.91</td><td>0.65</td><td>0.61</td><td>0.66</td></tr><tr><td>1976-1977-1984</td><td>0.85</td><td>0.76</td><td>0.8</td><td>0.88</td><td>0.86</td><td>0.88</td><td>0.9</td><td>0.61</td><td>0.59</td><td>0.65</td></tr><tr><td>1976-1977-1985</td><td>0.88</td><td>0.83</td><td>0.83</td><td>0.87</td><td>0.88</td><td>0.91</td><td>0.92</td><td>0.72</td><td>0.74</td><td>0.8</td></tr><tr><td>1976-1977-1986</td><td>0.81</td><td>0.75</td><td>0.72</td><td>0.87</td><td>0.83</td><td>0.91</td><td>0.91</td><td>0.67</td><td>0.62</td><td>0.66</td></tr><tr><td>1976-1977-1987</td><td>0.86</td><td>0.83</td><td>0.85</td><td>0.88</td><td>0.86</td><td>0.91</td><td>0.92</td><td>0.69</td><td>0.64</td><td>0.74</td></tr><tr><td>1976-1977-1988</td><td>0.84</td><td>0.79</td><td>0.83</td><td>0.89</td><td>0.85</td><td>0.89</td><td>0.92</td><td>0.76</td><td>0.74</td><td>0.78</td></tr><tr><td>1999-2000-2007</td><td>0.79</td><td>0.76</td><td>0.85</td><td>0.94</td><td>0.91</td><td>0.91</td><td>0.92</td><td>0.9</td><td></td><td>0.84</td></tr><tr><td>1999-2000-2008</td><td>0.7</td><td>0.67</td><td>0.72</td><td>0.89</td><td>0.86</td><td>0.84</td><td>0.91</td><td></td><td>0.81</td><td>0.83</td></tr><tr><td>1999-2000-2012</td><td>0.89</td><td>0.71</td><td>0.82</td><td>0.9</td><td>0.88</td><td>0.88</td><td>0.92</td><td>0.89</td><td>0.85</td><td></td></tr><tr><td>1999-2007-2008</td><td>0.81</td><td>0.72</td><td>0.8</td><td>0.94</td><td>0.87</td><td>0.89</td><td>0.91</td><td></td><td></td><td>0.87</td></tr><tr><td>1999-2007-2012</td><td>0.82</td><td>0.71</td><td>0.86</td><td>0.92</td><td>0.87</td><td>0.91</td><td>0.92</td><td>0.91</td><td></td><td></td></tr><tr><td>1999-2008-2012</td><td>0.65</td><td>0.51</td><td>0.73</td><td>0.86</td><td>0.78</td><td>0.65</td><td>0.87</td><td></td><td>0.82</td><td></td></tr><tr><td>2000-2007-2008</td><td>0.82</td><td>0.77</td><td>0.83</td><td>0.95</td><td>0.84</td><td>0.9</td><td>0.9</td><td>0.91</td><td></td><td>0.89</td></tr><tr><td>2000-2007-2012</td><td>0.81</td><td>0.74</td><td>0.85</td><td>0.95</td><td>0.89</td><td>0.9</td><td>0.92</td><td>0.89</td><td></td><td></td></tr><tr><td>2000-2008-2012</td><td>0.65</td><td>0.49</td><td>0.7</td><td>0.89</td><td>0.86</td><td>0.86</td><td>0.91</td><td></td><td>0.71</td><td></td></tr><tr><td>2007-2008-2012</td><td>0.83</td><td>0.72</td><td>0.81</td><td>0.94</td><td>0.86</td><td>0.88</td><td>0.9</td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_19.png" /></figure>

<table><thead><tr><th></th><th>dryer years</th><th>standard years</th><th>wetter years</th></tr></thead><tbody><tr><td colspan="4">Validation year</td></tr><tr><td>1976-1999-2003</td><td>0.79</td><td>0.77</td><td>0.89</td><td>0.87</td><td>0.81</td><td>0.42</td><td>0.88</td><td>0.72</td><td>0.73</td><td>0.79</td></tr><tr><td>1977-1996-2000</td><td>0.85</td><td>0.76</td><td>0.93</td><td>0.92</td><td>0.8</td><td>0.9</td><td>0.95</td><td>0.78</td><td>0.71</td><td>0.71</td></tr><tr><td>1978-1979-1999</td><td>0.85</td><td>0.7</td><td>0.85</td><td>0.79</td><td>0.73</td><td>0.86</td><td>0.88</td><td>0.77</td><td>0.73</td><td>0.76</td></tr><tr><td>1980-2000-2003</td><td>0.81</td><td>0.76</td><td>0.87</td><td>0.93</td><td>0.87</td><td>0.85</td><td>0.91</td><td>0.72</td><td>0.7</td><td>0.77</td></tr><tr><td>1981-1996-1999</td><td>0.97</td><td>0.72</td><td>0.92</td><td>0.84</td><td>0.85</td><td>0.87</td><td>0.9</td><td>0.76</td><td>0.75</td><td>0.77</td></tr><tr><td>1987-1982-2000</td><td>0.8</td><td>0.71</td><td>0.89</td><td>0.91</td><td>0.84</td><td>0.86</td><td>0.92</td><td>0.81</td><td>0.7</td><td>0.75</td></tr><tr><td>1983-1999-2003</td><td>0.82</td><td>0.6</td><td>0.88</td><td>0.86</td><td>0.9</td><td>0.87</td><td>0.89</td><td>0.78</td><td>0.71</td><td>0.75</td></tr><tr><td>1984-1999-2000</td><td>0.81</td><td>0.68</td><td>0.95</td><td>0.93</td><td>0.89</td><td>0.95</td><td>0.92</td><td>0.78</td><td>0.69</td><td>0.72</td></tr><tr><td>1987-1985-1999</td><td>0.88</td><td>0.7</td><td>0.84</td><td>0.82</td><td>0.85</td><td>0.88</td><td>0.88</td><td>0.76</td><td>0.76</td><td>0.77</td></tr><tr><td>1986-2000-2003</td><td>0.83</td><td>0.69</td><td>0.77</td><td>0.94</td><td>0.89</td><td>0.87</td><td>0.88</td><td>0.78</td><td>0.69</td><td>0.78</td></tr><tr><td>1987-1996-1999</td><td>0.83</td><td>0.63</td><td>0.71</td><td>0.84</td><td>0.89</td><td>0.88</td><td>0.9</td><td>0.78</td><td>0.76</td><td>0.76</td></tr><tr><td>1987-1988-2000</td><td>0.89</td><td>0.8</td><td>0.86</td><td>0.94</td><td>0.89</td><td>0.89</td><td>0.92</td><td>0.83</td><td>0.73</td><td>0.84</td></tr><tr><td>1989-1999-2003</td><td>0.73</td><td>0.56</td><td>0.87</td><td>0.88</td><td>0.8</td><td>0.87</td><td>0.87</td><td>0.75</td><td>0.76</td><td>0.78</td></tr><tr><td>1990-1996-2000</td><td>0.81</td><td>0.61</td><td>0.84</td><td>0.88</td><td>0.88</td><td>0.85</td><td>0.91</td><td>0.76</td><td>0.68</td><td>0.68</td></tr><tr><td>1991-1997-1999</td><td>0.88</td><td>0.73</td><td>0.86</td><td>0.85</td><td>0.89</td><td>0.88</td><td>0.89</td><td>0.73</td><td>0.77</td><td>0.7</td></tr><tr><td>1992-2000-2003</td><td>0.59</td><td>0.59</td><td>0.8</td><td>0.88</td><td>0.85</td><td>0.78</td><td>0.83</td><td>0.5</td><td>0.68</td><td>0.77</td></tr><tr><td>1993-1996-1999</td><td>0.42</td><td>0.61</td><td>0.87</td><td>0.88</td><td>0.88</td><td>0.87</td><td>0.92</td><td>0.79</td><td>0.76</td><td>0.82</td></tr><tr><td>1978-1994-2000</td><td>0.88</td><td>0.81</td><td>0.88</td><td>0.93</td><td>0.88</td><td>0.92</td><td>0.85</td><td>0.72</td><td>0.71</td><td>0.74</td></tr><tr><td>1995-1999-2003</td><td>0.76</td><td>0.55</td><td>0.88</td><td>0.86</td><td>0.91</td><td>0.88</td><td>0.91</td><td>0.77</td><td>0.75</td><td>0.78</td></tr><tr><td>1996-1997-2000</td><td>0.79</td><td>0.6</td><td>0.93</td><td>0.88</td><td>0.92</td><td>0.85</td><td>0.9</td><td>0.73</td><td>0.67</td><td>0.74</td></tr><tr><td>1978-1998-1999</td><td>0.89</td><td>0.76</td><td>0.88</td><td>0.91</td><td>0.89</td><td>0.89</td><td>0.9</td><td>0.83</td><td>0.73</td><td>0.8</td></tr><tr><td>2000-2001-2003</td><td>0.83</td><td>0.77</td><td>0.82</td><td>0.91</td><td>0.8</td><td>0.86</td><td>0.92</td><td>0.79</td><td>0.72</td><td>0.79</td></tr><tr><td>1996-1999-2002</td><td>0.81</td><td>0.56</td><td>0.91</td><td>0.87</td><td>0.88</td><td>0.89</td><td>0.89</td><td>0.78</td><td>0.76</td><td>0.76</td></tr><tr><td>1978-2000-2004</td><td>0.87</td><td>0.77</td><td>0.94</td><td>0.94</td><td>0.91</td><td>0.89</td><td>0.91</td><td>0.79</td><td>0.74</td><td>0.77</td></tr><tr><td>1977-1999-2003</td><td>0.87</td><td>0.76</td><td>0.87</td><td>0.84</td><td>0.87</td><td>0.9</td><td>0.93</td><td>0.78</td><td>0.75</td><td>0.77</td></tr><tr><td>1979-1996-2000</td><td>0.84</td><td>0.79</td><td>0.82</td><td>0.91</td><td>0.92</td><td>0.84</td><td>0.9</td><td>0.73</td><td>0.67</td><td>0.74</td></tr><tr><td>1978-1980-1999</td><td>0.87</td><td>0.75</td><td>0.83</td><td>0.84</td><td>0.70</td><td>0.87</td><td>0.9</td><td>0.82</td><td>0.73</td><td>0.77</td></tr><tr><td>1981-2000-2003</td><td>0.84</td><td>0.73</td><td>0.93</td><td>0.91</td><td>0.88</td><td>0.88</td><td>0.92</td><td>0.78</td><td>0.69</td><td>0.78</td></tr><tr><td>1982-1996-1999</td><td>0.84</td><td>0.79</td><td>0.91</td><td>0.88</td><td>0.91</td><td>0.88</td><td>0.92</td><td>0.8</td><td>0.76</td><td>0.77</td></tr><tr><td>1978-1983-2000</td><td>0.89</td><td>0.77</td><td>0.91</td><td>0.94</td><td>0.91</td><td>0.88</td><td>0.88</td><td>0.77</td><td>0.65</td><td>0.73</td></tr></tbody></table>

<figure><img src="image_20.png" /></figure>

3 training year - different cluster
NSE performance metric for LSTM models across 3 distinct scenarios

<figure><img src="image_21.png" /></figure>

* Median values:

* LSTM: Variable and higher with different clusters

* Superflex: Uniforme

* Range between the maximum and minimum values:

* LSTM: training years from different clusters has the smaller range

=> LSTM: better performance when trained using data from different clusters
* LSTM is sensitive to data size and hydrometeorological characteristics when compared to the Superflex.

* This sensitivity can be mitigated to some extent by training the model on large data size (15 years or greater).

* For smaller dataset, this sensitivity can be mitigated by training the model on diverse hydrometeorological conditions.
Thank You

**Reference :**

Fadi booodoo, Renaud Hostache, Carole Delenne, Nadia Skifa, Machine learning for rainfall-runoff modelling: influence of the training dataset length and characteristics on predictive accuracy, the Severn river in UK as a test case, Journal of Hydrology (submitted 2023).
Context:

* Rainfall-runoff modeling has a 170-year history starting with regression-based methods and gradually evolving to physically-based model, and then Conceptual models.

* Physical-based models provide a high accurate result but require a detailed understanding of the catchment characteristics and high computation cost.

* Conceptual models provide faster result with satisfying performances but requires more calibration data. Which makes them more suitable for real-time applications.

* Recently, the evolution of Artificial Neural Network (ANN) has significantly reshaped the field of rainfall-runoff modelling with the promises to be as fast and more accurate than Conceptual models.

* Prior research has primarily focused on evaluating the impact of training dataset size on the performance of ANNs models, with limited attention given to hydrometeorological conditions.

* Given climate change, it's becoming crucial to evaluate forecasting models' predictive abilities and flood patterns.
NSE performance metric for LSTM models across four distinct scenario

* Different cluster scenario still performs less effectively than the scenario with 15 successive training years

* Different cluster scenario generally achieves scores above 0.7 but often faces challenges in reaching the 0.8 threshold, while same cluster scenario exhibits a higher number of NSE values above 0.8 for wetter years compared to the different cluster scenario

=> The same cluster scenario performs exceptionally well on specific years while displaying relatively poor performance on others
<table><thead><tr><th>NSE properties</th><th>3 training years from the same cluster</th><th>3 training years from different clusters</th><th>3 successive training years</th><th>15 successive training years</th></tr></thead><tbody><tr><td>Mean NSE</td><td>0.76</td><td>0.82</td><td>0.73</td><td>0.89</td></tr><tr><td>Median NSE</td><td>0.81</td><td>0.84</td><td>0.73</td><td>0.89</td></tr><tr><td>Std NSE</td><td>0.13</td><td>0.08</td><td>0.12</td><td>0.05</td></tr><tr><td>Max-min NSE</td><td>0.60</td><td>0.40</td><td>0.65</td><td>0.17</td></tr><tr><td>All validation years :</td><td></td><td></td><td></td><td></td></tr><tr><td>NSE > 0.7</td><td>0.69</td><td>0.91</td><td>0.61</td><td>1.00</td></tr><tr><td>NSE > 0.8</td><td>0.47</td><td>0.60</td><td>0.31</td><td>0.90</td></tr><tr><td>Dryer years :</td><td></td><td></td><td></td><td></td></tr><tr><td>NSE > 0.7</td><td>0.67</td><td>0.77</td><td>0.56</td><td>1.00</td></tr><tr><td>NSE > 0.8</td><td>0.33</td><td>0.42</td><td>0.06</td><td>1.00</td></tr><tr><td>Standard years :</td><td></td><td></td><td></td><td></td></tr><tr><td>NSE > 0.7</td><td>0.95</td><td>1.00</td><td>0.87</td><td>1.00</td></tr><tr><td>NSE > 0.8</td><td>0.71</td><td>0.97</td><td>0.60</td><td>1.00</td></tr><tr><td>Wetter years :</td><td></td><td></td><td></td><td></td></tr><tr><td>NSE > 0.7</td><td>0.28</td><td>0.87</td><td>0.22</td><td>1.00</td></tr><tr><td>NSE > 0.8</td><td>0.14</td><td>0.10</td><td>0.00</td><td>0.67</td></tr></tbody></table>