River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya

Chebii, Sally J.; Mukolwe, Micah; Ong'or, Basil T. Iro

doi:10.2166/wpt.2022.034

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…This poses a health hazard to the local community. Injuries, infections and infectious disease outbreaks, poisoning, and increased mental health problems have all been reported after floods (Chebii et al, 2022). Displacements, shortages of safe water, injuries, loss of access to health care, and delayed recovery are all long-term health implications as shown in Figure 5 and Figure 6.…”

Section: Impacts After Floodingmentioning

confidence: 99%

The Risk of Flooding to Architecture and Infrastructure amidst a Changing Climate in Lake Baringo, Kenya

Nyakundi¹,

Eric²,

Kyeba³

2023

AJCC

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Climate change has grown more apparent in recent years with people becoming more aware of its potentially disastrous consequences. Flooding is one of the many consequences of a changing climate in Kenya known to cause immense devastation resulting in the loss of lives and property. This paper discusses the risk of flooding in Kenya as one of the many outcomes of climate change in the face of urgency to adapt Kenya's built environment to flooding which is likely to continue to prevail in the decades as a result of the looming climate change. It also sought to evaluate the physical, traumatic, and psychological effects on communities affected by flood events. This cross-sectional survey, both qualitative and quantitative in nature, executed between 13 th January 2021 and 14 th July 2021 with 132 respondents along the western shoreline of Lake Baringo, near Marigat Town focused on the flood levels, structures, their materials, and quantities. Results show that the area covered by Lake Baringo increased by 18% from 236 km 2 to 278 km 2 . The depth of floods ranged from 0.3 m to 1.2 m and exceeded 1.6 m during heavy rainfall up to 3.2 m with homes completely submerged by the lake. Flooding was experienced more by residents living in low areas nearer to the shoreline of the lake as compared to those living on higher grounds. 100% of the structures didn't have the architectural technology to withstand the impacts of flooding with 59% of housing made of corrugated iron sheets both on wall and roofing, 22% of mud houses roofed with either corrugated iron sheets, 10% being timber with thatch and only 8% stoned walled houses. This predisposed all the residents to the harmful impacts of flooding. Piled sandbags by locals as a mitigating measure proved inadequate to withstand the forces of the rising waters. Flood walls were built around local lodges near the lake but the rising water level quickly breached these defences. The study recommends that county and national governing authorities develop flood adapta-How to cite this paper: Victor, N., Eric, P., & Kyeba, K. ( 2023). The Risk of Flooding to Architecture and Infrastructure amidst a Changing Climate in Lake Baringo, Kenya.

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Section: Impacts After Floodingmentioning

confidence: 99%

The Risk of Flooding to Architecture and Infrastructure amidst a Changing Climate in Lake Baringo, Kenya

Nyakundi¹,

Eric²,

Kyeba³

2023

AJCC

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“…In addition, it is also based on research [20] and [21], mapping flood-prone areas is a condition that is climatological in nature, for those that are nowcasting or short-range forecasting it will be difficult to use mapping that is climatological in nature, especially in flood prediction. Research conducted by [22] have carried out simulations related to flood events but the variables used are only rainfall, water levels, and runoff so that areas far from rivers will have little or no flood probability values. Based on the background above, this research will be a reference for the community as well as the government and other agencies to take anticipatory and evacuation steps when a flood occurs in the future, especially for BMKG forecasters in providing early warning.…”

Section: Introductionmentioning

confidence: 99%

Prediction of flood events in the city of Bandar Lampung using the artificial neural network

2023

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The city of Bandar Lampung is currently experiencing seasonal flooding which occurs almost every year, resulting in significant losses. Floods recorded by BNPB in the last 10 years there were 16 incidents of flooding in the Bandar Lampung area. More than 14,000 people suffered, more than 500 people had to be evacuated, more than 900 houses were damaged, and 4 public facilities were damaged. To study the pattern of flood events in the past, the Artificial Neural Network Backpropagation learning method will be used which will utilize its non-linear variable learning abilities. The configuration settings for the Artificial Neural Network were carried out experimentally without any basis for assigning values, especially for the parameters of the number of hidden layers, number of neurons, and epochs used in training and variable testing. The results obtained from this study are the results of training and testing of datasets that have been carried out by ANN backpropagation are able to properly study patterns of flood events and also non-flood events in the dataset, this is evidenced by the results of high model configuration accuracy and also the results of predictive tables that able to describe actual conditions, setting the configuration model experimentally is able to produce an accuracy value of 90-100%, an average training correlation value of 0.96 and an average test correlation value of 0.89, and an average error value of 0.0089 out of 20 model configuration, and the flood prediction table are made based on the 1 best configuration with a training and testing accuracy rate of 100% with an error value of 0.00134, namely configuration model 20, the prediction table uses an average air temperature of 27˚C with 80% humidity. The prediction table is able to produce excellent flood potential results which are able to represent flood events as well as non-flood events based on the results of the dataset learning.

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River Water Flow Prediction Rate Based on Machine Learning Algorithms: A Case Study of Dez River, Iran

Goodarzi,

Niknam,

Barzkar

et al. 2023

Disaster Resilience and Green Growth

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River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya

Cited by 9 publications

References 19 publications

The Risk of Flooding to Architecture and Infrastructure amidst a Changing Climate in Lake Baringo, Kenya

The Risk of Flooding to Architecture and Infrastructure amidst a Changing Climate in Lake Baringo, Kenya

Prediction of flood events in the city of Bandar Lampung using the artificial neural network

River Water Flow Prediction Rate Based on Machine Learning Algorithms: A Case Study of Dez River, Iran

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