Integrating Hydrological–Hydraulic–AI (LSTM) Models for Improved Water Level Forecasting: Red River - Thai Binh Basin

Tri Doan Quang; Nhat Nguyen Van; Tuyet Quach Thi Thanh

doi:10.2478/cee-2026-0042

Integrating Hydrological–Hydraulic–AI (LSTM) Models for Improved Water Level Forecasting: Red River - Thai Binh Basin

Civil and Environmental Engineering

Volume 22 (2026): Issue 1 (March 2026)

By: Tri Doan Quang, Nhat Nguyen Van and Tuyet Quach Thi Thanh

Open Access

|Mar 2026

Figures & Tables

Sub-catchment delineation map for the MIKE NAM model

Hydraulic network of the Red - Thai Binh River

Results of model calibration in 2023: (a) Yen Bai station, (b) Ham Yen station, (c) Gia Bay station, (d) Thanh Son station, (e) Chu station

Table 1a:

Evaluation of errors in the MIKE NAM model

Station	Nash	R²	RMSE
Station	2023	2023	2023
Yen Bai	0.91	0.93	255.68
Ham Yen	0.68	0.69	86.62
Gia Bay	0.88	0.89	7.72
Thanh Son	0.88	0.91	66.32
Chu	0.86	0.89	96.88

Results of model validation in 2024: (a) Yen Bai station, (b) Ham Yen station, (c) Gia Bay station, (d) Thanh Son station, (e) Chu station

Table 1b:

Evaluation of errors in the MIKE NAM model

Station	Nash	R²	RMSE
Station	2024	2024	2024
Yen Bai	0.92	0.94	793.34
Ham Yen	0.96	0.98	288.86
Gia Bay	0.98	0.99	93.72
Thanh Son	0.9	0.93	117.43
Chu	0.94	0.95	213.4

Results of hydraulic model calibration in 2023: (a) Quang Cu station, (b) Son Tay station, (c) Ha Noi station, (d) Thuong Cat station, (e) Nam Dinh station, (f) Truc Phuong station, (g) Cat Khe station, (h) Quyet Chien station, (i) Trieu Duong station, (j) Cua Cam station

Table 2:

Evaluation of errors in the MIKE 11 hydraulic model

No.	Station	Nash		R²
No.	Station	2023	2024	2023	2024
1	Quang Cu	0.76	0.80	0.77	0.85
2	Son Tay	0.84	0.86	0.87	0.88
3	Ha Noi	0.88	0.89	0.89	0.90
4	Thuong Cat	0.89	0.87	0.89	0.90
5	Nam Dinh	0.91	0.91	0.94	0.92
6	Truc Phuong	0.91	0.88	0.95	0.90
7	Cat Khe	0.90	0.84	0.94	0.86
8	Quyet Chien	0.94	0.92	0.95	0.92
9	Trieu Duong	0.83	0.86	0.87	0.89
10	Cua Cam	0.90	0.78	0.92	0.80

Results of hydraulic model validation in 2024: (a) Quang Cu station, (b) Son Tay station, (c) Ha Noi station, (d) Thuong Cat station, (e) Nam Dinh station, (f) Truc Phuong station, (g) Cat Khe station, (h) Quyet Chien station, (i) Trieu Duong station, (j) Cua Cam station

Training process of the LSTM artificial intelligence model

Results of artificial intelligence model testing in 2024

Table 3:

Comparative evaluation of model errors

No.	Station	Nash	R²	RMSE
1	Quang Cu	0.994	0.994	6.7
2	Son Tay	0.98	0.988	41.4
3	Ha Noi	0.995	0.997	18.1
4	Thuong Cat	0.914	0.984	74.2
5	Nam Dinh	0.991	0.992	11.3
6	Truc Phuong	0.987	0.989	8.8
7	Cat Khe	0.993	0.996	12.8
8	Quyet Chien	0.996	0.996	8.2
9	Trieu Duong	0.99	0.993	15.6
10	Cua Cam	0.874	0.876	23.4

Water level forecasting toolkit interface

Table 4:

Evaluation of the forecasting results of the toolkit for Typhoon Wipha (07/2025)

No.	Station	Observed (cm)				Forecasting (cm)				Error (%)
No.	Station	t+6	t+12	t+18	t+24	t+6	t+12	t+18	t+24	t+6	t+12	t+18	t+24
1	Quang Cu	2738	2742	2734	2735	2736	2737	2743	2738	0.1	0.2	0.3	0.1
2	Hoa Binh	1202	1205	1210	1212	1203	1204	1211	1215	0.1	0.1	0.1	0.2
3	Lam Son	2113	2097	2089	2077	2094	2087	2079	2067	0.9	0.5	0.5	0.5
4	Son Tay	560	588	610	615	546	581	609	611	2.4	1.1	0.2	0.7
5	Ha Noi	420	452	484	490	410	435	473	485	2.3	3.7	2.3	0.9
6	Thuong Cat	358	392	432	434	347	384	438	447	3.0	2.1	1.3	3.0
7	Gia Bay	2221	2215	2205	2203	2216	2215	2203	2203	0.2	0.0	0.1	0.0
8	Chu	394	371	370	344	383	369	353	345	2.8	0.5	4.5	0.2
9	Binh Lieu	7781	7766	7758	7751	7778	7761	7755	7746	0.0	0.1	0.0	0.1
		Hmax		Hmin		Hmax		Hmin		Hmax		Hmin
10	Nam Dinh	283		199		281		203		0.7		2.0
11	Truc Phuong	267		135		261		140		2.2		3.7
12	Ben Binh	240		129		237		132		1.3		2.3
13	Cat Khe	243		155		235		157		3.3		1.3
14	Ba Nha	228		42		223		41		2.2		2.4
15	Quyet Chien	287		202		274		201		4.5		0.5
16	Trieu Duong	315		229		303		238		3.8		3.9
17	Ba Lat	230		10		234		11		1.7		10.0
18	Cua Cam	197		−48		192		−46		2.5		−4.2
19	Trung Trang	229		22		225		21		1.7		4.5

Table 5:

Evaluation of the forecasting results of the toolkit for Typhoon Kajiki (08/2025)

No.	Station	Observed (cm)				Forecasting (cm)				Error (%)
No.	Station	t+6	t+12	t+18	t+24	t+6	t+12	t+18	t+24	t+6	t+12	t+18	t+24
1	Quang Cu	2811	2849	2828	2798	2808	2810	2822	2827	0.1	1.4	0.2	1.0
2	Hoa Binh	964	1043	941	933	939	1005	962	923	2.6	3.7	2.3	1.0
3	Lam Son	2123	2106	2097	2091	2098	2097	2093	2089	1.2	0.4	0.2	0.1
4	Son Tay	673	701	710	698	651	698	692	691	3.2	0.4	2.5	1.0
5	Ha Noi	521	555	574	580	507	533	563	578	2.6	4.0	1.9	0.3
6	Thuong Cat	468	498	518	526	446	481	507	516	4.7	3.3	2.1	1.8
7	Gia Bay	2313	2332	2332	2301	2325	2327	2329	2305	0.5	0.2	0.1	0.2
8	Chu	820	938	830	699	824	949	822	674	0.5	1.1	1.0	3.6
9	Binh Lieu	7756	7749	7743	7738	7751	7744	7740	7734	0.1	0.1	0.0	0.1
		Hmax		Hmin		Hmax		Hmin		Hmax		Hmin
10	Nam Dinh	294		234		290		235		1.4		0.4
11	Truc Phuong	211		153		215		157		1.9		2.6
12	Ben Binh	269		225		269		224		0.1		0.4
13	Cat Khe	319		270		313		278		1.9		3.0
14	Ba Nha	157		129		156		126		0.6		2.3
15	Quyet Chien	300		240		293		240		2.3		0.1
16	Trieu Duong	344		284		348		278		1.2		2.1
17	Ba Lat	145		83		141		79		2.8		4.8
18	Cua Cam	79		26		74		28		6.3		7.7
19	Trung Trang	137		89		139		89		1.5		0.1

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DOI: https://doi.org/10.2478/cee-2026-0042 | Journal eISSN: 2199-6512 (formerly 1336-5835) | Journal ISSN: 1336-5835

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Language: English

Page range: 387 - 412

Submitted on: Sep 8, 2025

Accepted on: Sep 24, 2025

Published on: Mar 24, 2026

Published by: University of Žilina

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Water level forecasting,

Red River - Thai Binh Basin,

NAM-MIKE 11-LSTM,

Flood risk management

Related subjects:

Business and economics,

Business management,

Industries,

Environmental management

© 2026 Tri Doan Quang, Nhat Nguyen Van, Tuyet Quach Thi Thanh, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 22 (2026): Issue 1 (March 2026)

Integrating Hydrological–Hydraulic–AI (LSTM) Models for Improved Water Level Forecasting: Red River - Thai Binh Basin

Figures & Tables

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Table 1b:

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Table 5:

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