The Fort Worth Press - Landslide-prone Nepal tests AI-powered warning system

USD -
AED 3.672504
AFN 64.000368
ALL 80.878301
AMD 368.276037
ANG 1.789884
AOA 918.000367
ARS 1398.655759
AUD 1.37836
AWG 1.8025
AZN 1.70397
BAM 1.65809
BBD 2.008732
BDT 122.377178
BGN 1.668102
BHD 0.376584
BIF 2968.504938
BMD 1
BND 1.264635
BOB 6.891611
BRL 4.915095
BSD 0.997329
BTN 94.180832
BWP 13.389852
BYN 2.818448
BYR 19600
BZD 2.00585
CAD 1.36715
CDF 2265.000362
CHF 0.776955
CLF 0.022646
CLP 890.873638
CNY 6.80075
CNH 6.796265
COP 3727.014539
CRC 458.479929
CUC 1
CUP 26.5
CVE 93.480565
CZK 20.636704
DJF 177.601628
DKK 6.340404
DOP 59.310754
DZD 132.326735
EGP 52.744691
ERN 15
ETB 155.726591
EUR 0.84804
FJD 2.18304
FKP 0.733957
GBP 0.73346
GEL 2.67504
GGP 0.733957
GHS 11.234793
GIP 0.733957
GMD 73.503851
GNF 8750.794795
GTQ 7.614768
GYD 208.672799
HKD 7.83165
HNL 26.513501
HRK 6.393304
HTG 130.575219
HUF 300.190388
IDR 17377.45
ILS 2.901304
IMP 0.733957
INR 94.425504
IQD 1306.515196
IRR 1311500.000352
ISK 122.010386
JEP 0.733957
JMD 157.187063
JOD 0.70904
JPY 156.678504
KES 128.803357
KGS 87.420504
KHR 4001.526006
KMF 418.00035
KPW 899.983822
KRW 1461.810383
KWD 0.30766
KYD 0.831164
KZT 460.946971
LAK 21871.900301
LBP 89311.771438
LKR 321.097029
LRD 183.01047
LSL 16.361918
LTL 2.95274
LVL 0.60489
LYD 6.306642
MAD 9.121445
MDL 17.054809
MGA 4165.995507
MKD 52.257217
MMK 2099.83295
MNT 3581.379784
MOP 8.041456
MRU 39.863507
MUR 46.820378
MVR 15.403739
MWK 1729.049214
MXN 17.177604
MYR 3.921039
MZN 63.910377
NAD 16.361918
NGN 1365.000344
NIO 36.700437
NOK 9.209304
NPR 150.68967
NZD 1.675884
OMR 0.384681
PAB 0.997329
PEN 3.448264
PGK 4.404222
PHP 60.515038
PKR 277.958713
PLN 3.59545
PYG 6092.153787
QAR 3.645458
RON 4.426304
RSD 99.504048
RUB 74.240007
RWF 1462.082998
SAR 3.767486
SBD 8.019432
SCR 14.874401
SDG 600.503676
SEK 9.215704
SGD 1.267404
SHP 0.746601
SLE 24.650371
SLL 20969.496166
SOS 569.963122
SRD 37.399038
STD 20697.981008
STN 20.770633
SVC 8.727057
SYP 110.56358
SZL 16.351151
THB 32.203038
TJS 9.305159
TMT 3.5
TND 2.896867
TOP 2.40776
TRY 45.347504
TTD 6.759357
TWD 31.316038
TZS 2598.109449
UAH 43.809334
UGX 3737.018354
UYU 39.777881
UZS 12097.83392
VES 499.23597
VND 26308
VUV 118.45862
WST 2.707065
XAF 556.107838
XAG 0.012445
XAU 0.000212
XCD 2.70255
XCG 1.797465
XDR 0.69162
XOF 556.107838
XPF 101.106354
YER 238.625037
ZAR 16.38071
ZMK 9001.203584
ZMW 18.98775
ZWL 321.999592
  • CMSD

    0.1140

    23.534

    +0.48%

  • BCE

    -0.4300

    24.14

    -1.78%

  • CMSC

    0.1400

    23.11

    +0.61%

  • RIO

    2.2700

    105.38

    +2.15%

  • GSK

    -0.0900

    50.41

    -0.18%

  • BCC

    -2.0900

    70.67

    -2.96%

  • BTI

    0.2000

    58.28

    +0.34%

  • NGG

    0.9800

    86.89

    +1.13%

  • RELX

    0.0759

    33.58

    +0.23%

  • RBGPF

    0.7000

    63.61

    +1.1%

  • JRI

    0.0000

    13.15

    0%

  • RYCEF

    -0.4100

    16.37

    -2.5%

  • AZN

    0.3300

    182.85

    +0.18%

  • BP

    -0.4700

    43.34

    -1.08%

  • VOD

    0.5100

    16.2

    +3.15%

Landslide-prone Nepal tests AI-powered warning system
Landslide-prone Nepal tests AI-powered warning system / Photo: © AFP

Landslide-prone Nepal tests AI-powered warning system

Every morning, Nepali primary school teacher Bina Tamang steps outside her home and checks the rain gauge, part of an early warning system in one of the world's most landslide-prone regions.

Text size:

Tamang contributes to an AI-powered early warning system that uses rainfall and ground movement data, local observations and satellite imagery to predict landslides up to weeks in advance, according to its developers at the University of Melbourne.

From her home in Kimtang village in the hills of northwest Nepal, 29-year-old Tamang sends photos of the water level to experts in the capital Kathmandu, a five-hour drive to the south.

"Our village is located in difficult terrain, and landslides are frequent here, like many villages in Nepal," Tamang told AFP.

Every year during the monsoon season, floods and landslides wreak havoc across South Asia, killing hundreds of people.

Nepal is especially vulnerable due to unstable geology, shifting rainfall patterns and poorly planned development.

As a mountainous country, it is already "highly prone" to landslides, said Rajendra Sharma, an early warning expert at the National Disaster Risk Reduction and Management Authority.

"And climate change is fuelling them further. Shifting rainfall patterns, rain instead of snowfall in high altitudes and even increase in wildfires are triggering soil erosion," Sharma told AFP.

- Saving lives -

Landslides killed more than 300 people last year and were responsible for 70 percent of monsoon-linked deaths, government data shows.

Tamang knows the risks first hand.

When she was just five years old, her family and dozens of others relocated after soil erosion threatened their village homes.

They moved about a kilometre (0.6 miles) uphill, but a strong 2015 earthquake left the area even more unstable, prompting many families to flee again.

"The villagers here have lived in fear," Tamang said.

"But I am hopeful that this new early warning system will help save lives."

The landslide forecasting platform was developed by Australian professor Antoinette Tordesillas with partners in Nepal, Britain and Italy.

Its name, SAFE-RISCCS, is an acronym of a complex title -- Spatiotemporal Analytics, Forecasting and Estimation of Risks from Climate Change Systems.

"This is a low-cost but high-impact solution, one that's both scientifically informed and locally owned," Tordesillas told AFP.

Professor Basanta Adhikari from Nepal's Tribhuvan University, who is involved in the project, said that similar systems were already in use in several other countries, including the United States and China.

"We are monitoring landslide-prone areas using the same principles that have been applied abroad, adapted to Nepal's terrain," he told AFP.

"If the system performs well during this monsoon season, we can be confident that it will work in Nepal as well, despite the country's complex Himalayan terrain."

In Nepal, it is being piloted in two high-risk areas: Kimtang in Nuwakot district and Jyotinagar in Dhading district.

- Early warnings -

Tamang's data is handled by technical advisers like Sanjaya Devkota, who compares it against a threshold that might indicate a landslide.

"We are still in a preliminary stage, but once we have a long dataset, the AI component will automatically generate a graphical view and alert us based on the rainfall forecast," Devkota said.

"Then we report to the community, that's our plan."

The experts have been collecting data for two months, but will need a data set spanning a year or two for proper forecasting, he added.

Eventually, the system will deliver a continuously updated landslide risk map, helping decision makers and residents take preventive actions and make evacuation plans.

The system "need not be difficult or resource-intensive, especially when it builds on the community's deep local knowledge and active involvement", Tordesillas said.

Asia suffered more climate and weather-related hazards than any other region in 2023, according to UN data, with floods and storms the most deadly and costly.

And while two-thirds of the region have early warning systems for disasters in place, many other vulnerable countries have little coverage.

In the last decade, Nepal has made progress on flood preparedness, installing 200 sirens along major rivers and actively involving communities in warning efforts.

The system has helped reduce flooding deaths, said Binod Parajuli, a flood expert with the government's hydrology department.

"However, we have not been able to do the same for landslides because predicting them is much more complicated," he said.

"Such technologies are absolutely necessary if Nepal wants to reduce its monsoon toll."

D.Ford--TFWP