Review Article - (2022) Volume 6, Issue 3
Received: 24-May-2022, Manuscript No. JEH-22-64731;
Editor assigned: 27-May-2022, Pre QC No. JEH-22-64731(PQ);
Reviewed: 13-Jun-2022, QC No. JEH-22-64731;
Revised: 25-Jul-2022, Manuscript No. JEH-22-64731(R);
Published:
02-Aug-2022
, DOI: DOI: 10.37421/2684-4923.22.6.172
Citation: Ranaa, M Shohel, Nilufa Aktara, Kabir Hossaina
and Asmaul Hosnaa, et al. "Consequences of riverbank Erosion among
Rural Household along the River of Meghna in Bangladesh." J Environ
Hazard 6 (2022) : 172.
Copyright: © 2022 Rana MS, et al. This is an open-access article distributed under the terms of the creative commons attribution license which permits unrestricted use,
distribution and reproduction in any medium, provided the original author and source are credited.
Bangladesh, a riverine country, is suffering from acquit riverbank erosion which compels millions of her population to be displaced from their place of origin. Flood and riverbank erosion are dynamic and natural processes which have an adverse impact on livelihood. Our study focuses the socio-demographic profile of the victims of the study area. In our study we have a large number of the respondents receive the formal education that is also 60% of the respondents. Only 10% of respondents who finished the S.S.C. The damage caused by this disaster is a negative cause of illiteracy in the region. About 56% respondent says that river erosion has an impact on their child education. Most often after losing their home they have moved somewhere else therefore their child can’t attend the same school. More than 2 times the people about 54.9% are facing riverbank erosion and 98.8% of them are migrated to another place. After migration 66.4% of them are facing economic crisis and 82.6% of them have changed their occupations due to this calamity. Accommodations, education, treatment, are one of the basic needs which are in great trouble for those areas. Even after so much, only 70% of the people didn’t get any relief. Above all, 88% people believe that such kind of disasters can be solved by constructing embankments. There is a significant association between educational level and tackling erosion. In fact, caused riverbank erosion every year unemployment, landless and poverty are increasing which is responsible to country wide unstable condition.
Riverbank • Consequences • Erosion • Household • Migration
Riverbank erosion is considered one of the major natural disasters in Bangladesh, one of the adverse consequences of climate change. Riverbank erosion is a natural slow-onset hazard that upsets the balance of many fluvial and coastal environments of the world by wearing away the bank materials from a river’s banks. This geomorphic process results in the shorelines’ movement from its original position, affecting the lives of the people living nearby Islam and Rashid [1,2]. Bangladesh is a riverine country which is crisscrossed by more than 220 rivers, 57 of them are international, with a stretch of 2,400 kilometers (km) of bank-line [3]. All these rivers are divided into three major river systems i.e., the Ganges-Padma, the Brahmaputra-Jamuna and the Meghna. The catchment area of these major rivers is about 1.65 million km2 of which only 7.5 percent lies within the border of Bangladesh that generates 1,200 km3 of run-off annually, only 10 percent of which is generated within Bangladesh [4]. Bangladesh is susceptible to numerous natural disasters such as tropical cyclones, storm surges, coastal erosion, floods, droughts and riverbank erosion causes heavy loss of life and property. Furthermore, every year, natural calamities affect people’s lives and livelihoods in some parts of this country. Due to climatic and nonclimatic variability and change, life and livelihood of the general people of Bangladesh are heavily dependent on the local ecosystem; but their services are under immense pressure all over the country [5]. The Bangladesh is situated in the Bengal delta which was developed by three mighty rivers i.e., the Ganges, the Brahmaputra and the Meghna (GBM) [6]. The sediments deposited in the GBM basin is the highest in amount in the world [7]. factors that affect riverbank erosion are; decrease or increase in shear strength, changes of river course, characteristics of erosion prone bank and bed materials, pressure imbalance at the bank face, rapid drawdown, poor vegetation cover, obstacle in the streams, wind wave and boat wakes [8]. As such, 283 locations, 85 towns and growth centers, along with 2400 kilometers of riverbank line in Bangladesh are vulnerable to erosion. The union of this upazila is Char Falcon has 30,815 people who are suffering most due to river erosion. ‘Internally Displaced Populations’ (IDP) face many unavoidable problems at different stages of displacement. Displacement marginalized them in respect of livelihood patterns and psycho-physical troubles [9]. Erosion and accretion processes vary in a decade scale along the main rivers of Bangladesh, which can also be influenced by anthropogenic activities [10]. Storm surges, tidal ranges and wave action are the main reasons for erosion at the front of the Bay of Bengal in coastal Bangladesh. Erosion has significantly reduced many islands in Bangladesh over the past 100 years Riverbank erosion continues to create hardship and stress on the lives of people living in riverbank areas in Bangladesh [11,12]. Bangladesh, a riverine country, is suffering from acquit riverbank erosion which compels millions of her Population to be displaced from their place of origin. The Kamalnogorupazila of Laxmipur districts is the victim area of riverbank erosion of Meghna river. We have conducted our survey by primary data collection on 8 January, 2021 including 23 survey questions on the basis of their livelihood. In this area people have lost their home, land, hospital, educational institutions by the aggression of Meghna river. As such, 283 locations, 85 towns and growth centers, along with 2400 kilometers of riverbank line in Bangladesh are vulnerable to erosion. The union of this upazila is Char Falcon has 30,815 people who are suffering most due to river erosion. ‘Internally Displaced Populations’ (IDP) face many unavoidable problems at different stages of displacement. Displacement marginalized them in respect of livelihood patterns and psychophysical troubles. Riverbank erosion, a factor which accounts for the largest losses in Bangladesh, occur gradually but has long-term impacts and is not recoverable naturally. Riverbank Erosion is an important geo-morphological phenomenon affecting changes in river channel courses in alluvial plains creating long-term impacts and is not recoverable Naturally. This erosion problem is a regular phenomenon in Bangladesh and about 15 to 20 million people are at risk from the effects of erosion in the country while about 1 million people living in 94 upazila are directly affected by riverbank erosion every year. Newly settled people along with the people who have recently lost their home are interviewed to find out the difficulties associated with rearrangements for them. Along with this problem of displacement, effects on their income, local crime rate, effects on their family relations, sufferings, government effects to prevent erosion are revealed in this paper. Moreover, there is no effective plan of action to resolve this problem in Bangladesh [13].
Objective
• To study the social life of people affected by riverbank erosion.
• To study the impact of riverbank erosion and its effect on the people include not only income and expenditure poverty but also damage to other aspects of human socioeconomic development like, health, education, living conditions, and security.
• To determine the migration and resettlement patterns of Bangladesh.
This study took place in Chor Falkan union of Ramgati Upazila, Lakshmipur, Bangladesh. The study area was selected through field observation because the coastal area of Bangladesh is at great risk for its geographical uniqueness to be affected by riverbank erosion. A number of villages in Ramgati upazila of the district have already been devoured by the Meghna river. The erosion-hit villages are Aslipara, Ramgati Bazar and Ramdayal under Ramgati upazila and Nasirganj, Hajiganj of Charkalkini union, Kadir Panditerhat of Saheberhat union, Ludhua Folkon of Char Folkon union and Dakshin Folkon of Patarihat union in Kamalnagar upazila Lands are disappearing as erosion continues. Some 16,000 acres of crop lands, 15 educational institutions, cyclone shelter centers, four colonies of Ashrayan project, a stretch of 20 kilometer cross dam, roads, mosques and various structures have been devoured by the Meghna river in the last 40 years (locals informed). In this research method, individual’s information was collected through using a questionnaire where the respondents are asked questions and collected information. The data were collected through applying face-to-face interview techniques. Based on the Yamane sampling formula; where 95% confidence level and the margin of error (.05) were accepted. Altogether 81 samples were taken from 2691 households. And then adapting purposive sampling, the household heads were selected for interviews.
Statistical analysis
We perform descriptive analysis and graphical presentation, mean, standard deviation, Cramer’s V value chi-square test and meta analysis were used on socio-demographic factors to find out the association between various nominal and categorical variables. The t-test was applied to test the significance of the study variables. Finally, Binary Logistic Regression used to find out the individual expectation on dependent variables. We use the SPSS-25 version, MS Excel and MS-Word for performing analysis.
Md. Shohel Rana and Ayesha Meherun Nessa discussed in their study the impact of riverbank erosion on population migration and resettlement of Bangladesh. From this study it was observed that every year thousands of hectares of land were washed away by the aggressive rivers e.g., the Jamuna, the Padma, the Meghna. As a result thousands of people landless, homeless and lose their resources every year. In the study area some landless people leave without shelter and food to their own safe destination. Therefore to get out of this problem, some changes are needed such as honest political leaders, initiatives of institutions and service organizations that will work tirelessly for the victims of riverbank erosion [14].
Tanjinul Hoque Mollah and Jannatul Ferdaush defined migration as a significant part of human history as the movement of people over some distance and from one usual place of residence to another due to search of food, shelter or civilizing living conditions. Migration occurs both temporary and permanently. Temporary migration is migration to an area that is not intended to be permanent, for a specified and limited period of time, and usually undertaken for a specific purpose due to climate stress. Permanent migration occurs when the people landless, homeless and lose their resources [15].
Due to riverbank erosion millions of people are affected and the result is loss of housing, crops, cattle and also farmland. Bangladesh Water Development Board (BWDB) said the districts which are most in risk are Bogra, Sirajgonj, Kurigram, Lalmonirhat, Rangpur and Gaibandha [16]. Abdus Sattar Paloan convener of the organization said “Almost 50%of the kamalnagar and Rangamatiupazilas were washed away in twenty years. If emergency measures are not taken along with the dam construction, the very existence of Kamalganjupazila will be compromised [17].” BWDB Engineer of Laxmipur Md Arifur Hossain said: “We conducted a survey over a 15.5 kilometer area as part of the second phase. When we get the survey results, a design will be proposed to the authority, and the
construction will begin soon after [18].” Usually migration occurred from rural to urban area in search of livelihood but there is occurred internal migration to and fro because of riverbank erosion. Thousands acres fertile land, 35 schools, 15 markets outspent in Meghna river. Three major rivers (the Ganges, the Brahmaputra- Jamuna, the Meghna) are in Bangladesh 1.1 million acre feet are running in Bangladesh which occurred flooding, riverbank erosion, and house displacement. That’s why poverty, landlessness lag behind the rural people. The people who migrated to another area from the riverbank erosion area have suffered socially and economically in their current place.
The demographic data of the respondents show that there is a difference between the age of the respondents. We have 44% respondents between the ages of 15-35. Which means that the respondent is mostly young. We have some older respondents too. Though the number of the older respondents is only around 15%. Education which is one of the basic necessities of life can also promote the overall development of a society. In our study we have a large number of the respondents receive the formal education that is also 60% of the respondents. But only 10% of respondents who finished the S.S.C. We also found that the families mostly have 4-6 members. But there are also some families which have more than 7 members (Table 1).
Variable | Category | Frequency | Percentage (%) |
---|---|---|---|
Age of the people | 15-35 | 36 | 43.9 |
36-55 | 33 | 40.2 | |
56-75 | 13 | 15.9 | |
Total=82 | Total=100% | ||
Number of the family member | 1-3 | 5 | 6.1 |
4-6 | 46 | 56.1 | |
7-9 | 30 | 36.6 | |
>9 | 1 | 1.2 | |
Total=82 | Total=100% | ||
Education Level | Primary | 47 | 57.3 |
Under S.S.C | 27 | 32.9 | |
Under H.S.C | 8 | 9.8 | |
Total=82 | Total=100% |
Comment: Illustrates that 43.9% people ages between 15 to 35 years and 56.1% family have the number of family member are 4 to 6. The education level of 57.3% people is primary (Table 2).
Variable | Category | Frequency | Percentage (%) |
---|---|---|---|
Time erosion faced | 1 | 21 | 25.6 |
2 | 16 | 19.5 | |
>2 | 45 | 54.9 | |
Total=82 | Total=100% | ||
Losing home | Yes | 81 | 98.8 |
No | 1 | 1.2 | |
Total=82 | Total=100% | ||
Migrate after river erosion | Yes | 81 | 98.8 |
No | 1 | 1.2 | |
Total=82 | Total=100% | ||
Where migrated | Rural area | 50 | 61 |
Semi urban area | 30 | 36.6 | |
Urban area | 2 | 2.4 | |
Total=82 | Total=100% | ||
Changing occupation | Yes | 16 | 19.5 |
No | 66 | 80.5 | |
Total=82 | Total=100% | ||
Impact on income | Yes | 74 | 90.2 |
No | 8 | 9.8 | |
Total=82 | Total=100% | ||
Impact on education | Yes | 46 | 56.9 |
No | 36 | 43.1 | |
Total=82 | Total=100% | ||
Face economic crisis | Yes | 69 | 84.1 |
No | 13 | 15.9 | |
Total=82 | Total=100% | ||
Face identity crisis | Yes | 50 | 61 |
No | 32 | 39 | |
Total=82 | Total=100% | ||
Get any relief | Yes | 25 | 30.5 |
No | 57 | 69.5 | |
Total=82 | Total=100% | ||
Feel insecure | Yes | 41 | 50 |
No | 41 | 50 | |
Total=82 | Total=100% | ||
Government take preventing steps to reduce erosion | Yes | 37 | 45.1 |
No | 45 | 54.9 | |
Total=82 | Total=100% | ||
Cause of river erosion | Absence of embankment | 72 | 87.8 |
Abnormal flood | 10 | 12.2 | |
Total=82 | Total=100% | ||
How to tackle erosion | Construct embankment | 38 | 46.3 |
Need honest political leader | 38 | 46.3 | |
Need to consult local people | 1 | 1.2 | |
Need regular river dredging | 5 | 6.1 | |
Total=82 | Total=100% |
This table represents the frequency and the percentage of all questionnaire variables according to their classification.
Comment: Illustrates that about 54.9% people have faced the river erosion more than 2 times that’s why 98.8% people loss their home and migrated to another place. 80.5% people haven’t change their occupation but there occurs impact of income on 90.2% people and 84.1% people face economic crisis also 61% people face identity crisis.50% people feel insecure and 69% people don’t get any relief.
87.8% people thinks that the cause of this river erosion is absence of embankment but 54.9% people have said government don’t take any preventive steps to reduce erosion. To tackle this erosion 46.3%people have said they need honest political leaders and construct embankments (Table 3).
Descriptive statistics | ||||||
---|---|---|---|---|---|---|
N | Minimum | Maximum | Mean | Std. deviation | Variance | |
Age of people | 82 | 1 | 3 | 1.7195 | 0.72477 | 0.525 |
Number of family member | 82 | 1 | 4 | 2.3293 | 0.60969 | 0.372 |
Times of face erosion | 82 | 1 | 3 | 2.2927 | 0.85328 | 0.728 |
Migrate after river erosion | 82 | 0 | 1 | 0.0122 | 0.11043 | 0.012 |
Above Figure 1 shows that, age of the 44% respondents between 15-35, 40% and 16% respondents are between (35-55) and (56-75) respectively. In total, we have 84% of respondents who are aged between 15 and 55. So, we can say that most of the respondents are young.
This Figure 2 shows 46% of respondents who belong to (4-6) members families. More family members are a hindrance to the livelihood of the afflicted people, so they have to go out on the path of earning without education.
Above Figure 3 shows, most of the respondents (57.30%) who at least go to primary school. The damage caused by this disaster is a negative cause of illiteracy in the region (Figure 4).
From the above doughnut chart we saw that 61% of the respondent people who have lost their house due to river erosion migrated to the rural area (Figure 5).
From the graph we saw that 66 respondents out of 82 said that they have changed their occupation due to the river erosion (Figure 6).
From the Figure indicate that 56% respondent says that river erosion has an impact on their child education. Most often after losing their home they have moved somewhere else so that their child can’t attend the same school (Figure 7).
It has been found about 88% of the coastal people believe that, absence of the embankment is the main cause of the river erosion (Figure 8).
It has been found about 70% of the affected people didn’t get any relief (Figure 8).
Study area
The Kamalnogor upazila of Laxmipur districts is the victim area of riverbank erosion of Meghna river. The union of this upazila is Char Falcon has 30,815 people who have suffered most due to river erosion (Figure 9). ‘Internally Displaced Populations’ (IDP) face many unavoidable problems at different stages of displacement. Displacement marginalized them in respect of livelihood patterns and psycho-physical troubles (Table 4).
Variables in the equation | |||||||||
---|---|---|---|---|---|---|---|---|---|
B | S.E. | Wald | df | Sig. | Exp(B) | 95% C.I for EXP (B) | |||
Lower | Upper | ||||||||
Steps 1a | Times of face erosion | 0.233 | 2 | 0.89 | |||||
Times of face erosion (1) | -0.028 | 1.121 | 0.001 | 1 | 0.98 | 0.973 | 0.108 | 8.758 | |
Times of face erosion (2) | -0.37 | 0.883 | 0.176 | 1 | 0.675 | 0.691 | 0.122 | 3.898 | |
Losing home (1) | -20.426 | 40192.97 | 0 | 1 | 1 | 0 | 0 | ||
Migrate after river erosion (1) | -19.366 | 40192.97 | 0 | 1 | 1 | 0 | 0 | ||
Where migrate | 0.141 | 2 | 0.932 | ||||||
Where migrate (1) | -0.302 | 0.805 | 0.141 | 1 | 0.707 | 0.739 | 0.153 | 3.58 | |
Where migrate (2) | -18.961 | 28224.04 | 0 | 1 | 0.999 | 0 | 0 | . | |
Changing occupation (1) | 0.602 | 1.224 | 0.242 | 1 | 0.623 | 1.826 | 0.166 | 20.101 | |
Impact on income (1) | -18.676 | 13971.28 | 0 | 1 | 0.999 | 0 | 0 | ||
Effect on education (1) | -0.198 | 0.792 | 0.063 | 1 | 0.802 | 0.82 | 0.174 | 3.869 | |
Face economic crisis (1) | 0.509 | 0.965 | 0.279 | 1 | 0.598 | 1.664 | 0.251 | 11.036 | |
Face identity crisis (1) | 0.983 | 0.774 | 1.614 | 1 | 0.204 | 2.673 | 0.587 | 12.182 | |
Feel insecure (1) | 1.06 | 0.828 | 1.639 | 1 | 0.2 | 2.888 | 0.57 | 14.637 | |
Govt take preventing step to reduce erosion (1) | -0.591 | 0.791 | 0.558 | 1 | 0.455 | 0.554 | 0.117 | 2.612 | |
Constant | -2.832 | 1.421 | 3.972 | 1 | 0.046 | 0.059 |
Variable (s) entered on step 1: Times of face erosion, losing home, migration after river erosion, where migration, changing occupation, impact on income, effect on education, face economic crisis, face identity crisis, feel insecure, govt take preventative steps to reduce erosion.
Above Table illustrates that 97% of the people are facing river erosion 2 times due to riverbank erosion which occurs for absence of embankment and 73% people migrated to semi-urban area.82% people have changed their occupation and have harmful educational effects. 66% people face insecure after riverbank erosion which occurs for absence of embankment. 55% think that the govt. do not take preventive measure to reduce riverbank erosion which occurs for absence of embankment. Table 04 illustrates that 66.4% of the people who are affected by the riverbank erosion have faced economic crisis and 82.6% people have changed their occupation after migration (Table 5).
One sample t-test | N | Mean | Std. deviation | Std.error mean | Test value | 95% Confidence interval of the difference upper lower | t | Mean difference | df | Sig. (2-tailed) | |
---|---|---|---|---|---|---|---|---|---|---|---|
Times of face erosion | 82 | 2.2927 | 0.85328 | 0.09423 | 1.98 | 0.5002 | 0.1252 | 3.318 | 0.31268 | 81 | 0.001 |
The significant value 0.001 which is less than 0.05 (p-value) so the sample is unlike population (Table 6).
Case processing summary | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Cases | ||||||||||||
Valid | Missing | Total | ||||||||||
N | Percent | N | Percent | N | Percent | |||||||
education level* how to tackle erosion | 82 | 97.60% | 2 | 2.40% | 84 | 100.0% | ||||||
Education level*how to tackle erosion cross tabulation | ||||||||||||
how to tackle erosion | Total | |||||||||||
Construction embankment | Needed honest political leader | Needed to consult local people | needed regular river dredging | |||||||||
Education level | Primary | Count | 19 | 26 | 0 | 2 | 47 | |||||
Expected count | 21.8 | 21.8 | 0.6 | 2.9 | 47 | |||||||
% within education level | 40.40% | 55.30% | 0.00% | 4.30% | 100.00% | |||||||
% within how to tackle erosion | 50.00% | 68.40% | 0.00% | 40.00% | 57.30% | |||||||
Under ssc | Count | 14 | 12 | 0 | 1 | 27 | ||||||
Expected count | 12.5 | 12.5 | 0.3 | 1.6 | 27 | |||||||
% within education level | 51.90% | 44.40% | 0.00% | 3.70% | 100.00% | |||||||
% within how to tackle erosion | 36.80% | 31.60% | 0.00% | 20.00% | 32.90% | |||||||
Under hsc | Count | 5 | 0 | 1 | 2 | 8 | ||||||
Expected count | 3.7 | 3.7 | 0.1 | 0.5 | 8 | |||||||
% within education level | 62.50% | 0.00% | 12.50% | 25.00% | 100.00% | |||||||
% within how to tackle erosion | 13.20% | 0.00% | 100.00% | 40.00% | 9.80% | |||||||
Total | Count | 38 | 38 | 1 | 5 | 82 | ||||||
Expected count | 38 | 38 | 1 | 5 | 82 | |||||||
% within education level | 46.30% | 46.30% | 1.20% | 6.10% | 100.0% | |||||||
% within how to tackle erosion | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | |||||||
Chi-Square tests | ||||||||||||
Value | df | Asymptotic significance (2-sided) | ||||||||||
Pearson Chi-Square | 19.981a | 6 | 0.003 | |||||||||
Likelihood ratio | 17.015 | 6 | 0.009 | |||||||||
Linear-by-Linear association | 0.207 | 1 | 0.649 | |||||||||
N of valid cases | 82 |
8 cells (66.7%) are expected to count less than 5. The minimum expected count is 0.10.
Here, p value (0.003) <level of significance(0.05), so educational level and erosion tackle efficiency significantly related. We conclude that there is significant association between educational level and tackle erosion (Table 7).
Case processing summary | ||||||||
---|---|---|---|---|---|---|---|---|
Cases | ||||||||
Valid | Missing | Total | ||||||
N | Percent | N | Percent | N | Percent | |||
Times of face erosion*get any relief | 82 | 97.60% | 2 | 2.40% | 84 | 100.00% | ||
Times of face erosion*get any relief crosstabulation | ||||||||
Get any relief | Total | |||||||
yes | no | |||||||
Times of face erosion | 1 | Count | 12 | 9 | 21 | |||
Expected count | 6.4 | 14.6 | 21 | |||||
% within times of face erosion | 57.10% | 42.90% | 100.00% | |||||
% within get any relief | 48.00% | 15.80% | 25.60% | |||||
2 | Count | 2 | 14 | 16 | ||||
Expected count | 4.9 | 11.1 | 16 | |||||
% within times of face erosion | 12.50% | 87.50% | 100.00% | |||||
% within get any relief | 8.00% | 24.60% | 19.50% | |||||
>2 | Count | 11 | 34 | 45 | ||||
Expected count | 13.7 | 31.3 | 45 | |||||
% within times of face erosion | 24.4% | 75.6% | 100.0% | |||||
% within get any relief | 44.00% | 59.60% | 54.90% | |||||
Total | Count | 25 | 57 | 82 | ||||
Expected count | 25 | 57 | 82 | |||||
% within times of face erosion | 30.50% | 69.50% | 100.00% | |||||
% within get any relief | 100.00% | 100.00% | 100.00% | |||||
Chi-Square tests | ||||||||
Value | df | Asymptotic Significance (2-sided) | ||||||
Pearson Chi-Square | 10.259a | 2 | 0.006 | |||||
Likelihood ratio | 10.058 | 2 | 0.007 | |||||
Linear-by-Linear association | 5.467 | 1 | 0.019 | |||||
N of valid cases | 82 |
1 cell (16.7%) have expected count less than 5. The minimum expected count is 4.88.
Here, p value (0.006)<level of significance (0.05), so time of face erosion and getting any relief significantly related. we conclude that there is significant association between time of face erosion and getting any relief (Table 8).
Case processing summary | ||||||
---|---|---|---|---|---|---|
Cases | ||||||
Valid | Missing | Total | ||||
N | Percent | N | Percent | N | Percent | |
where migrate*changing occupation | 82 | 97.60% | 2 | 2.40% | 84 | 100.00% |
where migrate*changing occupation cross tabulation | ||||||
Changing occupation | Total | |||||
yes | no | |||||
Where migrate | Rural area | Count | 15 | 35 | 50 | |
Expected count | 9.8 | 40.2 | 50 | |||
% within where migrate | 30.00% | 70.00% | 100.00% | |||
% within changing occupation | 93.80% | 53.00% | 61.00% | |||
Semi urban area | Count | 1 | 29 | 30 | ||
Expected count | 5.9 | 24.1 | 30 | |||
% within where migrate | 3.30% | 96.70% | 100.00% | |||
% within changing occupation | 6.30% | 43.90% | 36.60% | |||
Urban area | Count | 0 | 2 | 2 | ||
Expected count | 0.4 | 1.6 | 2 | |||
% within where migrate | 0.00% | 100.00% | 100.00% | |||
% within changing occupation | 0.00% | 3.00% | 2.40% | |||
Total | Count | 16 | 66 | 82 | ||
Expected count | 16 | 66 | 82 | |||
% within where migrate | 19.50% | 80.50% | 100.00% | |||
% within changing occupation | 100.00% | 100.00% | 100.00% | |||
Chi-Squaretests | ||||||
Value | df | Asymptotic significance (2-sided) | ||||
Pearson Chi-Square | 8.987a | 2 | 0.011 | |||
Likelihood ratio | 11.09 | 2 | 0.004 | |||
Linear-by-Linear association | 8.353 | 1 | 0.004 | |||
N of valid cases | 82 |
2 cells (33.3%) have expected count less than 5. The minimum expected count is 0.39.
Here, p value (0.011) <level of significance (0.05), so place of migration and changing occupation significantly related.we conclude that there is significant association between place of migration and changing occupation (Table 9).
Case processing summary | ||||||
---|---|---|---|---|---|---|
Cases | ||||||
Valid | Missing | Total | ||||
N | Percent | N | Percent | N | Percent | |
Where migrate*get any relief | 82 | 97.60% | 2 | 2.40% | 84 | 100.00% |
Where migrate*get any relief Crosstabulation | ||||||
get any relief | Total | |||||
yes | no | |||||
Where migrate | Rural area | Count | 21 | 29 | 50 | |
Expected count | 15.2 | 34.8 | 50 | |||
% within where migrate | 42.00% | 58.00% | 100.00% | |||
% within get any relief | 84.00% | 50.90% | 61.00% | |||
Semi urban area | Count | 4 | 26 | 30 | ||
Expected count | 9.1 | 20.9 | 30 | |||
% within where migrate | 13.30% | 86.70% | 100.00% | |||
% within get any relief | 16.00% | 45.60% | 36.60% | |||
Urban area | Count | 0 | 2 | 2 | ||
Expected count | 0.6 | 1.4 | 2 | |||
% within where migrate | 0.00% | 100.00% | 100.00% | |||
% within get any relief | 0.00% | 3.50% | 2.40% | |||
Total | Count | 25 | 57 | 82 | ||
Expected count | 25 | 57 | 82 | |||
% within where migrate | 30.50% | 69.50% | 100.00% | |||
% within get any relief | 100.00% | 100.00% | 100.00% | |||
Chi-Squaretests | ||||||
Value | df | Asymptotic significance (2-sided) | ||||
Pearson Chi-Square | 8.170a | 2 | 0.017 | |||
Likelihood ratio | 9.261 | 2 | 0.01 | |||
Linear-by-Linear association | 7.902 | 1 | 0.005 | |||
N of valid cases | 82 |
2 cells (33.3%) have expected count less than 5. The minimum expected count is 0.61.
Here, p value (.017) <level of significance (0.05), so place of migration and getting any relief significantly related. We conclude that there is significant association between place of migration and get ant relief (Table 10).
Where migrate*number of family member cross tabulation | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Number of family member | Total | |||||||||
01-03 | 04-06 | 07-09 | 4 | |||||||
Where migrate | Rural area | Count | 3 | 33 | 13 | 1 | 50 | |||
Expected count | 3 | 28 | 18.3 | 0.6 | 50 | |||||
% within where migrate | 6.00% | 66.00% | 26.00% | 2.00% | 100.00% | |||||
% within number of family member | 60.00% | 71.70% | 43.30% | 100.00% | 61.00% | |||||
Semi urban area | Count | 1 | 12 | 17 | 0 | 30 | ||||
Expected count | 1.8 | 16.8 | 11 | 0.4 | 30 | |||||
% within where migrate | 3.30% | 40.00% | 56.70% | 0.00% | 100.00% | |||||
% within number of family member | 20.00% | 26.10% | 56.70% | 0.00% | 36.60% | |||||
Urban area | Count | 1 | 1 | 0 | 0 | 2 | ||||
Expected count | 0.1 | 1.1 | 0.7 | 0 | 2 | |||||
% within where migrate | 50.00% | 50.00% | 0.00% | 0.00% | 100.00% | |||||
% within number of family member | 20.00% | 2.20% | 0.00% | 0.00% | 2.40% | |||||
Total | Count | 5 | 46 | 30 | 1 | 82 | ||||
Expected count | 5 | 46 | 30 | 1 | 82 | |||||
% within where migrate | 6.10% | 56.10% | 36.60% | 1.20% | 100.00% | |||||
% within number of family member | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% | |||||
Case processing summary | ||||||||||
Cases | ||||||||||
Valid | Missing | Total | ||||||||
N | Percent | N | Percent | N | Percent | |||||
Where migrate *number of family member | 82 | 97.60% | 2 | 2.40% | 84 | 100.00% | ||||
Chi-Square tests | ||||||||||
Pearson Chi-Square | Value | df | Asymptotic Significance (2-sided) | |||||||
Likelihood ratio | 15.181a | 6 | 0.019 | |||||||
Linear-by-Linear association | 12.271 | 6 | 0.056 | |||||||
N of valid cases | 0.885 | 1 | 0.347 | |||||||
Pearson Chi-Square | 82 |
8 cells (66.7%) have expected to count less than 5. The minimum expected count is .02.
Here, p value (.019) <level of significance (0.05), so place of migration and number of family members significantly related. We conclude that there is significant association between place of migration and number of family members (Table 11)
Case processing summary | ||||||||
---|---|---|---|---|---|---|---|---|
Cases | ||||||||
Valid | Missing | Total | ||||||
N | Percent | N | Percent | N | Percent | |||
Changing occupation* effect on education | 82 | 97.60% | 2 | 2.40% | 84 | 100.00% | ||
Changing occupation*effect on education crosstabulation | ||||||||
Effect on education | Total | |||||||
yes | no | |||||||
Changing occupation | Yes | Count | 13 | 3 | 16 | |||
Expected count | 9 | 7 | 16 | |||||
% within changing occupation | 81.30% | 18.80% | 100.00% | |||||
% within effect on education | 28.30% | 8.30% | 19.50% | |||||
No | Count | 33 | 33 | 66 | ||||
Expected count | 37 | 29 | 66 | |||||
% within changing occupation | 50.00% | 50.00% | 100.00% | |||||
% within effect on education | 71.70% | 91.70% | 80.50% | |||||
Total | Count | 46 | 36 | 82 | ||||
Expected count | 46 | 36 | 82 | |||||
% within changing occupation | 56.10% | 43.90% | 100.00% | |||||
% within effect on education | 100.00% | 100.00% | 100.00% | |||||
Chi-Squaretests | ||||||||
Value | df | Asymptotic significance (2-sided) | Exact Sig. (2-sided) | Exact Sig. (1-sided) | ||||
Pearson Chi-Square | 5.106a | 1 | 0.024 | |||||
Continuity correction | 3.916 | 1 | 0.048 | |||||
Likelihood ratio | 5.516 | 1 | 0.019 | |||||
Fisher's exact test | 0.027 | 0.021 | ||||||
Linear-by-Linear association | 5.044 | 1 | 0.025 | |||||
N of valid cases | 82 |
0 cells (0.0%) have expected count less than 5. The minimum expected count is 7.02.
Here, p value (.009) <level of significance (0.05), so face economic crisis and get any relief significantly related. We conclude that there is significant association between economical crisis and get any relief (Tables 12 and 13).
Case processing summary | ||||||||
---|---|---|---|---|---|---|---|---|
Cases | ||||||||
Valid | Missing | Total | ||||||
N | Percent | N | Percent | N | Percent | |||
changing occupation*get any relief | 82 | 97.60% | 2 | 2.40% | 84 | 100.00% | ||
changing occupation * get any relief crosstabulation | ||||||||
get any relief | Total | |||||||
yes | no | |||||||
changing occupation | Yes | Count | 12 | 4 | 16 | |||
Expected count | 4.9 | 11.1 | 16 | |||||
% within changing occupation | 75.00% | 25.00% | 100.00% | |||||
% within get any relief | 48.00% | 7.00% | 19.50% | |||||
No | Count | 13 | 53 | 66 | ||||
Expected count | 20.1 | 45.9 | 66 | |||||
% within changing occupation | 19.70% | 80.30% | 100.00% | |||||
% within get any relief | 52.00% | 93.00% | 80.50% | |||||
Total | Count | 25 | 57 | 82 | ||||
Expected count | 25 | 57 | 82 | |||||
% within changing occupation | 30.50% | 69.50% | 100.00% | |||||
% within get any relief | 100.00% | 100.00% | 100.00% | |||||
Chi-Squaretests | ||||||||
Value | df | Asymptotic significance (2-sided) | Exact sig. (2-sided) | Exact sig. (1-sided) | ||||
Pearson Chi-Square | 6.776a | 1 | .009 | |||||
Continuity correction | 5.174 | 1 | .023 | |||||
Likelihood ratio | 10.496 | 1 | .001 | |||||
Fisher's exact test | .008 | .005 | ||||||
Linear-by-Linear association | 6.693 | 1 | .010 | |||||
N of valid cases | 82 |
1 cell (25.0%) have expected count less than 5. The minimum expected count is 3.96.
Computed only for a 2 x 2 table
Case processing summary | ||||||
---|---|---|---|---|---|---|
Cases | ||||||
Valid | Missing | Total | ||||
N | Percent | N | Percent | N | Percent | |
Number of family member*get any relief | 82 | 97.60% | 2 | 2.40% | 84 | 100.00% |
Number of family member* get any relief crosstabulation | ||||||
Get any relief | Total | |||||
Yes | No | |||||
Number of family member | 1-3 | Count | 1 | 4 | 5 | |
Expected count | 1.5 | 3.5 | 5 | |||
% within number of family member | 20.00% | 80.00% | 100.00% | |||
% within get any relief | 4.00% | 7.00% | 6.10% | |||
4-6 | Count | 20 | 26 | 46 | ||
Expected count | 14 | 32 | 46 | |||
% within number of family member | 43.50% | 56.50% | 100.00% | |||
% within get any relief | 80.00% | 45.60% | 56.10% | |||
7-9 | Count | 3 | 27 | 30 | ||
Expected count | 9.1 | 20.9 | 30 | |||
% within number of family member | 10.00% | 90.00% | 100.00% | |||
% within get any relief | 12.00% | 47.40% | 36.60% | |||
4.00 | Count | 1 | 0 | 1 | ||
Expected count | 0.3 | 0.7 | 1.0 | |||
% within number of family member | 100.00% | 0.00% | 100.00% | |||
% within get any relief | 4.00% | 0.00% | 1.20% | |||
Total | Count | 25 | 57 | 82 | ||
Expected count | 25 | 57 | 82 | |||
% within number of family member | 30.50% | 69.50% | 100.00% | |||
% within get any relief | 100.00% | 100.00% | 100.00% | |||
Chi-Squaretests | ||||||
Value | df | Asymptotic significance (2-sided) | ||||
Pearson Chi-Square | 12.144a | 3 | 0.007 | |||
Likelihood ratio | 13.357 | 3 | 0.004 | |||
Linear-by-Linear association | 2.772 | 1 | 0.096 | |||
N of valid case | 82 |
4 cells (50.0%) have expected count less than 5. The minimum expected count is .30
Here, p value (.009) <level of significance (0.05), so face economic crisis and get any relief significantly related. We conclude that there is significant association between economical crisis and get any relief.
Here, p value (.007) <level of significance (0.05), so number of family members and get any relief significantly related. We conclude that there is significant association between family members and get any relief (Table 14).
Education level | How to tackle erosion | ||
---|---|---|---|
Education level | Pearson correlation | 1 | 0.051 |
Sig. (2-tailed) | 0.652 | ||
N | 82 | 82 | |
How to tackle erosion | Pearson correlation | 0.051 | 1 |
Sig. (2-tailed) | 0.652 | ||
N | 82 | 82 | |
Correlations | |||
Get any relief | Times of face erosion | ||
Get any relief | Pearson correlation | 1 | 0.260* |
Sig. (2-tailed) | 0.018 | ||
N | 82 | 82 | |
Times of face erosion | Pearson correlation | 0.260* | 1 |
Sig. (2-tailed) | 0.018 | ||
N | 82 | 82 | |
*Correlation is significant at the 0.05 level (2-tailed). | |||
Correlations | |||
Where migrate | Changing occupation | ||
Where migrate | Pearson correlation | 1 | 0.321** |
Sig. (2-tailed) | 0.003 | ||
N | 82 | 82 | |
Changing occupation | Pearson correlation | 0.321** | 1 |
Sig. (2-tailed) | 0.003 | ||
N | 82 | 82 |
**Correlation is significant at the 0.01 level (2-tailed).
Where r=0.051, which implies that educational level of respondents and tackle erosion weekly related to each other with positive slope. Highly educated people contribute more to tackling other natural disasters including riverbank erosion than less educated people.
Where r=0.260, which implies that time of erosion and get any relief weekly related to each other with positive slope. So we conclude that people get any relief increase gradually due to the number of times that erosion occurs.
Where r=0.321, which implies that place of migration and changing occupation weekly related to each other with positive slope. So we conclude that people with disabilities choose different professions due to relocation.
Meta-analysis
Let us consider,
H0: Facing river erosion is independent of migration after erosion and education level.
H1: Facing river erosion depends on migration after erosion and education level.
We conduct the test at 5% level of significance (Table 15).
Migrate after erosion | |||||||
---|---|---|---|---|---|---|---|
yes | no | ||||||
Face erosion | |||||||
Education level | 1 times | 2 times | >2 times | Total | 1 times | 2 times | total |
Primary | 11.05 | 8.84 | 22.11 | 42 | 0.52 | 0.48 | 1 |
Under SSC | 6.84 | 5.47 | 13.68 | 26 | 11.96 | 11.04 | 23 |
Under HSC | 2.11 | 1.68 | 4.21 | 8 | 0.52 | 0.48 | 1 |
Total | 20 | 16 | 40 | 76 | 13 | 12 | 25 |
Test statistic
x2=oij2/Eij-n
Now the table of expected frequency is given below:
For migrate after erosion:
x1 2=122/11.05+92/8.84+212/22.11+72/6,84+4^2/5.47+152/13.68 +12 /2.11+32/1.68+42/4.21- 76
=78.308-76
=2.308 (P value, P1= 0.679313) The result is not significance at P<0.05
As P1=0.679313>0.05 so H0 is accepted.
For not migrate after erosion:
x2 2=12/0.52+02/0.48+112/11.96+ 122/11.04+12/0.52+02/0.48-25 =2.006 (P value, P2=0.366777)
The result is not significance at P<0.05
AS P2=0.366777>0.05 so accept H0.
For conclusion using data of all migrate or not migrate meta analysis can be performed by combining the P values of x2 where combined P value is given by
-2ln P=-2ln (P1+P2)
=-2ln (0.679313*0.366777)
=-2ln (0.2491563842)
=2.779349061
Thus -2lnP is distributed as x2 with 2K=(2*2) (K=2) df. The tabulated value of x2 at 5% level of significance with 4 df is 9.49 which is greater than x2 (-2ln P). So that, H0 is accepted.
The calculation can also be done by combining the values of calculated for migrate and not migrate where combined x2 is
x2=x1 2+x2 2
=2.308+ 2.006
=4.314
This x2 has (4+2)=6 df. The tabulated value at 5% level of significance is 12.59 which are greater than combined x2. So that, H0 is accepted.
So this indicates that facing erosion is independent of migration after erosion and education level.
Our study focuses the socio-demographic profile of the victims of the study area. In our study we have a large number of the respondents receive the formal education that is also 60% of the respondents. Riverbank Erosion is an important geo-morphological phenomenon affecting changes in river channel courses in alluvial plains creating long-term impacts and is not recoverable Naturally.
[Crossref]
[Crossref][Googlescholar] Swapan SI. Meghna River Erosion:People in Lakshmipur at risk as dam awaits completion. DhakaTribune, Dhaka. 2019.
Journal of Environmental Hazards received 40 citations as per Google Scholar report