Berliner Boersenzeitung - Landslide-prone Nepal tests AI-powered warning system

EUR -
AED 4.228872
AFN 71.972068
ALL 95.909842
AMD 434.62105
ANG 2.060869
AOA 1055.922261
ARS 1612.664041
AUD 1.626132
AWG 2.075573
AZN 1.962349
BAM 1.950864
BBD 2.321646
BDT 141.447046
BGN 1.897259
BHD 0.434591
BIF 3421.857394
BMD 1.151497
BND 1.469501
BOB 7.96509
BRL 6.015764
BSD 1.152694
BTN 106.183656
BWP 15.53909
BYN 3.398317
BYR 22569.334493
BZD 2.318365
CAD 1.568033
CDF 2507.959919
CHF 0.903603
CLF 0.026455
CLP 1044.636615
CNY 7.906464
CNH 7.925002
COP 4261.550951
CRC 543.330067
CUC 1.151497
CUP 30.514661
CVE 109.985776
CZK 24.434471
DJF 205.274212
DKK 7.472194
DOP 70.41277
DZD 152.14506
EGP 60.26191
ERN 17.27245
ETB 179.932431
FJD 2.545929
FKP 0.859123
GBP 0.862707
GEL 3.126354
GGP 0.859123
GHS 12.489347
GIP 0.859123
GMD 84.64225
GNF 10105.34523
GTQ 8.839097
GYD 241.164032
HKD 9.012851
HNL 30.512273
HRK 7.534821
HTG 150.989955
HUF 389.892131
IDR 19472.95998
ILS 3.606085
IMP 0.859123
INR 106.44101
IQD 1510.053265
IRR 1522019.494717
ISK 144.385837
JEP 0.859123
JMD 180.413545
JOD 0.816388
JPY 183.355687
KES 148.831121
KGS 100.697856
KHR 4626.275212
KMF 490.537296
KPW 1036.385217
KRW 1720.37028
KWD 0.353567
KYD 0.960595
KZT 564.217802
LAK 24695.163427
LBP 103228.165394
LKR 358.385716
LRD 210.95726
LSL 19.043312
LTL 3.40007
LVL 0.696529
LYD 7.357322
MAD 10.802176
MDL 20.016878
MGA 4777.973736
MKD 61.615023
MMK 2418.166226
MNT 4111.007847
MOP 9.292973
MRU 45.808704
MUR 52.864827
MVR 17.790309
MWK 1998.877461
MXN 20.552114
MYR 4.521965
MZN 73.591629
NAD 19.042487
NGN 1603.874006
NIO 42.424139
NOK 11.142746
NPR 169.893849
NZD 1.964862
OMR 0.442747
PAB 1.152724
PEN 3.944657
PGK 4.971379
PHP 68.561306
PKR 322.020359
PLN 4.26854
PYG 7463.1826
QAR 4.202604
RON 5.093645
RSD 117.390523
RUB 91.720314
RWF 1685.280067
SAR 4.320981
SBD 9.264001
SCR 15.257101
SDG 692.049195
SEK 10.754691
SGD 1.472235
SHP 0.863921
SLE 28.314872
SLL 24146.308417
SOS 657.650391
SRD 43.027403
STD 23833.655954
STN 24.438382
SVC 10.086393
SYP 127.674885
SZL 19.048221
THB 37.022348
TJS 11.04889
TMT 4.030238
TND 3.388926
TOP 2.772528
TRY 50.798269
TTD 7.822277
TWD 36.760144
TZS 2993.891239
UAH 51.039225
UGX 4315.120012
USD 1.151497
UYU 46.092982
UZS 13988.486971
VES 503.96085
VND 30255.574683
VUV 137.716839
WST 3.12565
XAF 654.298751
XAG 0.01351
XAU 0.000224
XCD 3.111977
XCG 2.077516
XDR 0.812706
XOF 654.335594
XPF 119.331742
YER 274.741289
ZAR 19.283306
ZMK 10364.857819
ZMW 22.392028
ZWL 370.781454
  • RBGPF

    0.1000

    82.5

    +0.12%

  • CMSC

    -0.1250

    23.115

    -0.54%

  • RYCEF

    -0.5500

    16.95

    -3.24%

  • NGG

    1.6400

    91.33

    +1.8%

  • RELX

    -0.1300

    34.63

    -0.38%

  • GSK

    -1.1100

    54.04

    -2.05%

  • VOD

    -0.0450

    14.355

    -0.31%

  • BTI

    0.1550

    59.315

    +0.26%

  • RIO

    -0.6850

    91.395

    -0.75%

  • BCC

    -1.7700

    70.13

    -2.52%

  • CMSD

    -0.0670

    23.083

    -0.29%

  • BCE

    -0.0750

    25.815

    -0.29%

  • JRI

    0.1500

    13

    +1.15%

  • BP

    0.9000

    42.46

    +2.12%

  • AZN

    -1.3050

    192.005

    -0.68%

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."

(S.G.Stein--BBZ)