Dubai Telegraph - Landslide-prone Nepal tests AI-powered warning system

EUR -
AED 4.181853
AFN 71.737344
ALL 94.207554
AMD 418.322713
ANG 2.038723
AOA 1044.183684
ARS 1684.219261
AUD 1.652043
AWG 2.051075
AZN 1.935121
BAM 1.954504
BBD 2.295478
BDT 140.187076
BGN 1.925397
BHD 0.429715
BIF 3384.956268
BMD 1.138695
BND 1.474722
BOB 7.87578
BRL 5.889215
BSD 1.139745
BTN 106.97609
BWP 15.488733
BYN 3.305509
BYR 22318.42614
BZD 2.292181
CAD 1.615985
CDF 2581.998711
CHF 0.922298
CLF 0.02669
CLP 1050.435044
CNY 7.741021
CNH 7.746498
COP 3916.712983
CRC 517.457002
CUC 1.138695
CUP 30.175423
CVE 110.191959
CZK 24.252899
DJF 202.95547
DKK 7.474822
DOP 66.965612
DZD 151.930292
EGP 56.43875
ERN 17.080428
ETB 183.746703
FJD 2.580392
FKP 0.862766
GBP 0.862704
GEL 3.011847
GGP 0.862766
GHS 12.850482
GIP 0.862766
GMD 83.124857
GNF 9986.380487
GTQ 8.695236
GYD 238.521895
HKD 8.929682
HNL 30.494786
HRK 7.533497
HTG 148.96126
HUF 354.082932
IDR 20310.906483
ILS 3.41842
IMP 0.862766
INR 107.447907
IQD 1493.010352
IRR 1565990.589223
ISK 143.999498
JEP 0.862766
JMD 179.501017
JOD 0.807318
JPY 184.189074
KES 147.427206
KGS 99.579138
KHR 4574.967464
KMF 494.193463
KPW 1024.826089
KRW 1749.752789
KWD 0.352551
KYD 0.94977
KZT 552.993446
LAK 25016.417765
LBP 102061.847887
LKR 383.106057
LRD 207.60239
LSL 18.734582
LTL 3.362271
LVL 0.688786
LYD 7.31615
MAD 10.687216
MDL 20.207605
MGA 4820.80451
MKD 61.594172
MMK 2390.41825
MNT 4076.111956
MOP 9.206597
MRU 45.48585
MUR 54.338532
MVR 17.593515
MWK 1976.290008
MXN 19.940761
MYR 4.655003
MZN 72.758607
NAD 18.734582
NGN 1569.96453
NIO 41.942198
NOK 11.324352
NPR 171.161545
NZD 2.018867
OMR 0.437826
PAB 1.139745
PEN 3.886424
PGK 5.001685
PHP 69.797448
PKR 317.183953
PLN 4.287814
PYG 6956.388929
QAR 4.154446
RON 5.241443
RSD 117.302246
RUB 89.917486
RWF 1669.093634
SAR 4.280063
SBD 9.16872
SCR 16.007589
SDG 683.217725
SEK 11.087566
SGD 1.474047
SHP 0.850151
SLE 28.229626
SLL 23877.873405
SOS 651.368238
SRD 42.681693
STD 23568.691856
STN 24.483771
SVC 9.97239
SYP 125.86237
SZL 18.723589
THB 38.053992
TJS 10.548108
TMT 3.985433
TND 3.378061
TOP 2.741705
TRY 53.089497
TTD 7.745866
TWD 36.281069
TZS 2994.762678
UAH 51.15779
UGX 4183.227131
USD 1.138695
UYU 45.749675
UZS 13689.925577
VES 706.848451
VND 29947.684055
VUV 135.743206
WST 3.166577
XAF 655.522484
XAG 0.019442
XAU 0.000281
XCD 3.07738
XCG 2.054038
XDR 0.81526
XOF 655.522484
XPF 119.331742
YER 271.721169
ZAR 18.754541
ZMK 10249.624729
ZMW 20.530391
ZWL 366.659393
  • CMSC

    -0.1160

    21.93

    -0.53%

  • BCC

    1.2600

    81.02

    +1.56%

  • JRI

    0.2100

    12.79

    +1.64%

  • CMSD

    -0.1600

    21.77

    -0.73%

  • RIO

    -1.3700

    93.74

    -1.46%

  • NGG

    -0.4100

    83.01

    -0.49%

  • GSK

    0.6100

    52.5

    +1.16%

  • BCE

    -0.2800

    22.92

    -1.22%

  • RBGPF

    3.7000

    65

    +5.69%

  • RELX

    0.4200

    31.34

    +1.34%

  • RYCEF

    0.3900

    18.39

    +2.12%

  • VOD

    0.0300

    13.89

    +0.22%

  • BTI

    0.2800

    62.76

    +0.45%

  • AZN

    2.7300

    188.41

    +1.45%

  • BP

    -0.5900

    37.13

    -1.59%

Landslide-prone Nepal tests AI-powered warning system
Landslide-prone Nepal tests AI-powered warning system / Photo: Prakash MATHEMA - 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."

F.Damodaran--DT